Abusive Coworker Treatment,Coworker Support,and Employee Turnover

Abstract

Hostile treatment from others at work can have undesirable consequences. The present study aims to validate this proposition by examining whether abusive coworker treatment is a significant predictor of employee turnover among entry-level employees in the hospitality industry. With a sample of 979 restaurant servers, this research examined the impact of abusive coworker treatment along with coworker emotional and instrumental support on turnover over a 6-month period. Abusive coworker treatment was significantly related to higher turnover among newcomers but not among experienced employees. Furthermore, coworker emotional support was related to lower turnover among all employees in the sample. These findings provide a more nuanced perspective on the role of abusive coworker treatment and coworker support in combating the perennial turnover challenge in the hospitality industry.

Keywords

abusive coworker treatment coworker support employee turnover

Without question, employee turnover represents one of the most significant human resource management (HRM) challenges in the hospitality industry. Tracey and Hinkin (2010) highlight that turnover is estimated at 60% annually in lodging (Woods, Heck, & Sciarini, 1998) and up to 120% for employees in quick-service restaurants. This excessive churn of employees presents a host of challenges for operators and managers, such as reduced service quality, excessive replacement and training costs, and reduced morale among stayers (Tracey & Hinkin, 2010). Ellingson, Tews, and Dachner (2016) review a variety of factors that are reputed to account for such high turnover, including routine job responsibilities, limited employee autonomy, relatively poor compensation, and few opportunities for promotion to enhance long-term retention. Even though there may be many obstacles to modifying these HRM practices in hospitality enterprises, one area where there may be more flexibility to promote retention is through relationships with coworkers. People may not only be critical in providing a high-quality service experience but may also be critical in promoting retention.

As a step forward in helping to solve the turnover challenge in the hospitality industry, a number of recent studies have illustrated that coworkers are important in reducing turnover. For example, Tews, Michel, and Allen (2014) demonstrated that coworker socializing and fun in the workplace reduced turnover, and Tews, Stafford, and Michel (2014) provided evidence that constituent (i.e., coworker) attachment curbed the extent to which negative life events lead to turnover. In addition, Felps et al. (2009) provided support for the proposition that employees are less apt to leave when their coworkers are more embedded in the organization. Furthermore, Karatepe (2012) found that employees report lower turnover intentions when employees receive greater coworker support.

While previous research has focused on the positive aspects of coworker relationships in reducing turnover, the “dark side” of coworkers has largely been neglected. The popular press highlights the prevalence and detrimental impact of negative coworker treatment. For example, Psychology Today has called workplace bullying a silent epidemic, arguing that such maltreatment leads to increased stress among employees, higher rates of absenteeism, and greater turnover (Williams, 2011). According to Forbes, citing a Zogby International survey, approximately 37% of employees are targets of harassment (Van Dusen, 2008). Of those subject to such abuse, 45% experienced adverse health consequences, such as cardiovascular problems, an impaired immune system, and anxiety. While previous research has demonstrated that a variety of forms of adverse coworker treatment is related to lower job satisfaction and higher turnover intentions (Bowling & Beehr, 2006; Spector & Jex, 1998), the relationship between abusive coworker treatment and actual turnover has yet to be fully explored.

To advance the growing body of research on social influences and turnover, the present study will examine three interrelated issues. One, this research will assess the extent to which abusive coworker treatment affects turnover among entry-level employees. Two, this research will evaluate whether abusive coworker treatment influences turnover differently for newcomers versus experienced employees. Three, this research will explore how much abusive coworker treatment influences turnover relative to coworker emotional and instrumental support. Through this study, we hope to provide a more complete picture of what affects turnover and retention in the hospitality industry and offer evidence-based prescriptions for applied practice.

Theoretical Background and Hypotheses

Parallel with Tepper’s (2000) definition of abusive supervision, we characterize abusive coworker treatment as nonphysical hostility perpetrated by coworkers. Such treatment includes loud outbursts, undermining, belittling, giving somebody the silent treatment, being rude, and similar forms of maltreatment. Abusive coworker treatment is a form of nonsexual workplace harassment, which Bowling and Beehr (2006) define as interpersonal behavior aimed at intentionally harming other employees. Extreme harassment can include physical assault; while minor harassment includes obscene gestures, dirty looks, threats, yelling, giving the silent treatment, and belittling. Abusive coworker treatment reflects the latter, minor harassment. Although it may lead to less severe outcomes, abusive coworker treatment, as a form of minor harassment, occurs more frequently than physical aggression (LeBlanc & Kelloway, 2002). Abusive coworker treatment may lead to greater turnover as it affects more individuals in the workplace.

Coworkers are certainly important in any organizational context but particularly so in the hospitality industry. While individuals may work alone from time to time, the experience of work is often a social endeavor. Most jobs involve working with others, teamwork, and cooperation. Furthermore, work is not always about work, and individuals actively seek out their peers for socializing and friendship. The work for entry-level employees, in particular, is not confined to an isolated office. Individuals frequently work alongside one another, making interpersonal relationships a defining characteristic of one’s work life.

Mossholder, Settoon, and Henagan (2005) contend that high-quality interpersonal relationships “enmesh individuals within a relational web, making them less susceptible to forces that could dislodge them from their organization” (p. 608). Validating this notion, Mossholder et al. demonstrated that having a strong social network on the job is central in promoting retention. In a similar vein, Maertz and Campion (2004) and Maertz and Griffeth (2004) articulated that constituent attachment is one of the key drivers of employee retention. When employees develop meaningful ties with others at work, greater constituent attachment will result. In a hospitality context, Ellingson et al. (2016) demonstrated that constituent attachment was significantly related to lower turnover and that the effect for constituent attachment on turnover was stronger than other motivational forces often believed to affect retention, such as job satisfaction, pay satisfaction, affective commitment, normative commitment, and continuance commitment (Maertz & Campion, 2004; Maertz & Griffeth, 2004).

Regarding the “dark side” of interpersonal treatment at work, a variety of studies have demonstrated that such treatment leads to negative consequences for individuals and organizations. Using the superordinate label of harassment, Bowling and Beehr (2006) conducted a meta-analysis on studies of nonsexual harassment, aggression, abuse, interpersonal conflict, and bullying without distinguishing coworker, supervisor, and outsider referents. Bowling and Beehr’s results demonstrated that harassment leads to less favorable employee attitudes, higher incidents of counterproductive work behavior, and greater turnover intentions, among other adverse consequences. Nielsen and Einarsen’s (2012) meta-analysis, narrowly focused on bullying, demonstrated similar results. Herschcovis and Barling’s (2010) meta-analysis isolated studies that were specifically related to coworker maltreatment, using the superordinate label aggression for such behavior. In descending order, significant relationships were demonstrated for interpersonal deviance (r = .47), organizational deviance (r = .29), job satisfaction (r = −.25), depression (r = .24), turnover intentions (r = .23), and affective commitment (r = −.20). While Tepper (2000) analyzed the effect of abusive supervision on actual turnover and Hogh, Hoel, and Carneiro (2011) analyzed the effects of all types of bullying on turnover, it is clear that the relationship between abusive coworker treatment and actual turnover has yet to be fully explored.

Self-determination theory (Deci & Ryan, 2001; Ryan & Deci, 2000) is offered as a lens through which to view the reasons why abusive coworker treatment could lead to greater turnover. According to self-determination theory, the degree to which individuals experience optimal functioning depends on satisfying three innate needs: the needs for autonomy, competence, and relatedness (Deci & Ryan, 2001). The need for autonomy relates to the need to be self-regulating and control one’s decisions and behavior. The need for competence is the desire to be effective in one’s pursuits and exhibit mastery in one’s endeavors. The need for relatedness is characterized as a need to connect with others and develop effective interpersonal relationships. A number of studies in work settings have demonstrated that meeting these needs is related to employee satisfaction and well-being (Gagné & Deci, 2005; Ryan & Deci, 2017). When employees are better able to satisfy their needs for autonomy, competence, and relatedness in the workplace, they may be more committed to their organizations and less likely to quit.

However, in the face of greater abusive coworker treatment, need fulfillment may be compromised. Regarding the need for autonomy, when coworkers engage in abusive behaviors such as ridiculing employees’ actions, individuals may feel as though they cannot act independently without fear of negative repercussions. Because employees likely seek to avoid negative behavior directed toward them, they may behave within more narrowly defined parameters, thus constraining their perceptions of being autonomous. Regarding the need for competence, when employees are criticized by their coworkers, they may internalize the belief that they are not effective in their work, which would limit their sense of mastery and achievement. Finally, as abusive coworker treatment is a reflection of low-quality peer relationships, employees’ need for relatedness would likely be unfulfilled when experiencing coworker maltreatment. When coworkers make negative comments about employees to others, give employees the silent treatment, and put them down in front of others, individuals likely do not feel welcome in their work groups. In short, in the face of abusive coworker treatment, employees may not obtain what they desire from work, perceive a lack of fit, and seek to remove themselves from the workplace through quitting. Hostility rather than hospitality may be just as harmful for employees as it is for the guest.

Hypothesis 1: Abusive coworker treatment will be positively related to employee turnover.

Even though abusive coworker treatment could exhibit an adverse influence on turnover in general, we argue that such treatment is more detrimental for newer employees. The focus for most newcomers is acquiring the knowledge and skills to be successful in completing their job responsibilities, learning the social norms of the workplace, and developing a network of social support (Chao, O’Leary-Kelly, Wolf, Klein, & Gardner, 1994). During this period of adjustment, individuals may especially value and need the support of their coworkers. In fact, a number of previous studies have validated the importance of positive coworker relationships in helping individuals adapt to their new workplaces (Allen, McManus, & Russell, 1999; Bauer, Bodner, Erdogan, Truxillo, & Tucker, 2007; Morrison, 2002). Since newcomers may experience feelings of uncertainty and anxiety, they may be especially sensitive to maltreatment from their peers. Newcomers could be particularly vulnerable to negative social influences as they make mistakes inherent in learning new knowledge and skills and test the waters in establishing a peer group. After individuals have learned the ropes and established a network of social support; however, they may be more immune to the negative effects of abusive coworker treatment. Although abusive coworker treatment may never be a good thing, it may exhibit a more detrimental effect on turnover among newcomers.

Hypothesis 2: Abusive coworker treatment will exhibit a stronger positive relationship with turnover for newcomers than for more experienced employees.

Beforehand it was argued that favorable coworker influences would embed employees and promote retention. Thus, there is interest in examining abusive coworker treatment relative to coworker support, the assistance employees receive from others in the workplace (Karasek & Theorell, 1990). Previous research has found mixed results for the impact of coworker support in reducing turnover. In support of this relationship, Fisher (1985) demonstrated a significant relationship between coworker support and turnover with a sample of nurses. However, Iverson (1999), Iverson and Pullman (2000), and Mossholder et al. (2005), also with samples of health care employees, failed to do so. More recent research by Tews, Michel, and Ellingson (2013) demonstrated that different dimensions of coworker support relate to turnover in different ways among a sample of hospitality employees. Their research distinguished between emotional support and instrumental support. Emotional support is person focused, grounded in friendship, and personal concern. Instrumental support is oriented toward helping individuals complete their job responsibilities (Beehr, Jex, Stacy, & Murray, 2000). The results from Tews et al.’s (2013) research indicated that emotional support was related to lower turnover, while instrumental support was related to higher turnover.

Drawing on the work of Tews et al. (2013), emotional support is hypothesized to exhibit a negative relationship with turnover, while instrumental support is hypothesized to exhibit a positive relationship. Emotional support may be important because it leads to the development of friendships, which could be especially salient among entry-level hospitality employees. Employees in the hospitality industry are disproportionately younger (U.S. Bureau of Labor Statistics, 2015), and younger individuals are more apt to seek friendships through work given their life stage as they establish their adult identities and because they have fewer marital and other family obligations outside of work (Erikson, 1968; Tokuno, 1986). Emotional support may also be important because it could help with the challenges of emotional labor (Duke, Goodman, Treadway, & Breland, 2009; Glomb & Tews, 2004). Challenging service encounters have the potential to drain one’s emotional resources, and emotional support could facilitate one’s emotional recovery (Sonnentag, 2003), where one’s overall sense of well-being is restored. Regarding instrumental support, it may be positively related to turnover because employees could interpret the receipt of such support as meaning they possess a lack of competence. The work for entry-level employees is relatively low skill. Employees who are on the receiving end of more instrumental support might be those who are less competent in their work and less able to perform well during rush periods of high customer demand or when staffing levels are kept at a minimum. Employees who receive greater instrumental support may very well be aware that they receive more help than their peers, and they may perceive a lack of competence. Therefore, such employees could be more likely to self-select out of the organization due a lack of person-job fit, actual or perceived.

Hypothesis 3: Coworker emotional support will exhibit a negative relationship with turnover.

Hypothesis 4: Coworker instrumental support will exhibit a positive relationship with turnover.

While coworker emotional and instrumental support could affect turnover, the impact of abusive coworker treatment on turnover is proposed to be larger in magnitude. This proposition is based on the large body of research extending across a variety of academic disciplines that has demonstrated the asymmetry of positive and negative influences on behavior (Camerer, Loewenstein, & Rabin, 2011; Gilovich, Griffin, & Kahneman, 2002; Kahneman, Slovic, & Tversky, 1982; Thaler, 2015). Abusive coworker treatment may be more potent than coworker support since negative events elicit more rapid and prominent responses than positive events (Carretié, Mercado, Tapia, & Hinojosa, 2001; Rozin & Royzman, 2001) and negative influences tend to dominate decision making more so than positive influences (Ito, Larsen, Smith, & Cacioppo, 1998). Thus, abusive coworker relationships could be dominant in influencing decisions whether to remain with or leave the organization. That is, the detrimental effect of insults, belittling, and the like may be stronger than the benefits of coworker emotional support, such as taking time to listen to employees and otherwise being friendly. Furthermore, even though coworker instrumental support may be related to higher turnover as discussed above, instrumental support is still support and would likely not be as detrimental as abusive coworker treatment. Based on these arguments, we hypothesize

Hypothesis 5: Abusive coworker treatment will exhibit a stronger relationship with turnover than will coworker emotional and instrumental support.

Method

Sample and Procedure

The sample for this study included 979 restaurant servers from a casual dining restaurant chain in the United States. The servers were drawn from 78 restaurants, with approximately 11 to 12 servers from each restaurant. The sample was 68% female, 77% Caucasian, and on average 27 years old. At the beginning of the study, the servers had been employed in their restaurants 2.77 years. All servers in the organization were invited to be involved in the study. Data on the independent variables were obtained through the servers’ responses to a paper-and-pencil survey. At the beginning of the study, the servers received a packet from the research team that included the survey itself, information about the study, instructions for survey completion, and an assurance of confidentiality. Each survey contained a unique identifier used to link survey responses with subsequent turnover or retention. Once completed, the participants sealed their surveys in envelopes, which were then forwarded to the research team. Of the 2,056 servers invited to participate, 1,029 individuals completed surveys with useable data, reflecting a response rate of 50%. Six months was used as the time frame over which to examine turnover, consistent with Tews et al.’s (2013) support and turnover research. Turnover was ascertained following survey administration by accessing organization records. Specifically, a report of leavers and stayers was provided to the research team from the organization’s corporate office via spreadsheet. Of the initial 1,029 respondents, 50 were removed from the sample for this research because they were terminated, resulting in the final sample of 979.

Measures

Abusive Coworker Treatment

Harris, Harvey, and Kacmar’s (2011) 6-item version of Tepper’s (2000) 15-item abusive supervision scale with a coworker referent shift was used to assess abusive coworker treatment. Sample items included: My coworkers put me down in front of others and My coworkers express anger at me when they are mad for another reason. The respondents indicated the extent to which they agreed with each statement using a 5-point scale (1 = strongly disagree, 5 = strongly agree). The internal consistency reliability estimate for the scale was .84. The distribution of the scale was skewed right, whereby the mean was greater than the mode. The participants largely reported low frequencies of abuse with fewer individuals reporting higher frequencies. To help normalize the distribution and further satisfy the assumptions for regression, the scale was transformed using a square-root transformation (Cohen, Cohen, West, & Aiken, 2003).

Coworker Emotional and Instrumental Support

Tews et al.’s (2013) adaptation of Settoon and Mossholder’s (2002) measures were used to measure coworker emotional and instrumental support. Six items were used to measure emotional support. Sample items included: My coworkers take a personal interest in me and My coworkers listen to me when I have to get something off my chest. Five items were used to measure instrumental support. Sample items included: My coworkers assist me with heavy workloads and My coworkers help me when things get demanding. The respondents indicated the extent to which they agreed with each statement with a 5-point scale (1 = strongly disagree, 5 = strongly agree). The internal consistency reliability estimates for both the emotional and instrumental support scales were .90.

A confirmatory factor analysis was performed to examine the discriminant validity of the three coworker predictors. Specifically, each scale item was loaded onto its respective abusive coworker treatment, coworker emotional support, or coworker instrumental support dimension. This three-factor model fit the data well. Although the model possessed a statistically significant chi-square test, χ²(116, n = 978) = 849.66, p < .01, the individual fit indices provide support for the three-factor model, comparative fit index (CFI) = .92; Tucker–Lewis index (TLI) = .91; root mean square error of approximation (RMSEA) = .08 (90% confidence interval [.08, .09]); and standardized root mean square residual (SRMR) = .04. Table 1 presents the factor loadings of the scale items, where all items loaded .60 or higher on their respective constructs.

Table 1.

Factor Analysis of Abusive Coworker Treatment and Coworker Support.

	M	SD	Abusive coworker treatment	Coworker emotional support	Coworker instrumental support
Abusive coworker treatment
Make negative comments about me to others	1.80	0.98	.72
Give me the silent treatment	1.44	0.71	.68
Express anger at me when they are mad for another reason	1.98	1.08	.68
Are rude to me	1.78	0.80	.69
Break promises they make	1.66	0.80	.63
Put me down in front of others	1.47	0.76	.71
Coworker emotional support
Listen to me when I have to get something off my chest	3.63	1.17		.75
Take time to listen to my concerns	3.76	1.00		.85
Take a personal interest in me	3.74	1.08		.77
Try to cheer me up when I’m having a bad day	3.97	1.09		.75
Make an effort to make me feel welcome in the work group	4.09	0.89		.75
Treat me as one of the team	4.10	0.90		.76
Coworker instrumental support
Help me when things get demanding	3.73	0.83			.81
Help me with difficult assignments, even when I don’t directly ask for help	3.28	1.18			.82
Assist me with heavy workloads	3.72	1.05			.81
Help me when I’m running behind my in work	3.77	0.84			.77
Go out of their way to help me with work problems	3.48	1.08			.81

Note. Factor loadings are standardized estimates.

Stage of Employment

Those employed with the organization less than 6 months at the beginning of the study were classified as newcomers, and those employed at least 6 months or longer were classified as experienced employees. This operationalization parallels that employed by Tracey, Sturman, and Tews (2007) who examined differences in predictors of job performance between newcomers and experienced individuals. Tracey et al. argued that new entry-level employees in the hospitality industry are learning their jobs for up to 6 months, during which they are focused on learning their fundamental task requirements and becoming socialized into their organizations. Based on this classification, 298 of the participants in this study were categorized as newcomers, and 681 were categorized as experienced employees. Newcomers were coded 1, and experienced employees were coded 0.

Voluntary Turnover

This research focused on employee turnover that was voluntary, where employees left the organization of their own volition rather than being terminated. Voluntary turnover was coded dichotomously as 1 for leavers (those who left the organization within the 6-month period) and 0 for stayers (those who remained employed with the organization during the 6-month period). As mentioned beforehand, 50 of the 1,029 respondents who completed surveys at the beginning of the study were removed from the sample because their turnover was involuntary. At the end of the study period, 315 employees were leavers, and 664 employees were stayers. The resulting voluntary turnover rate was 32%.

Control Variables

Employee age, gender, and ethnicity were used as control variables. Age and gender have consistently been found to predict turnover in previous research (Griffeth, Hom, & Gaertner, 2000). Ethnicity was also included as some have suggested that minorities are more likely to leave than nonminorities (Hom & Griffeth, 1995). Gender was coded 1 for female and 0 for male. Ethnicity was coded 1 for Caucasian and 0 for other ethnic groups.

Analytic Strategy

Random coefficient modeling (RCM), using nonlinear and linear mixed effects and the open-source platform R, was used to test the hypothesized relationships (Pinheiro & Bates, 2000). A RCM analysis was performed because the study participants were nested within 78 different restaurants. The ICC(1) estimate for turnover was .02. Although only 2% of the total variance in turnover is explained by restaurant location, testing the hypothesized relationships with RCM provides more accurate estimates of the standard errors when analyzing nested data and reduces the risk of type II errors for the individual-level effects and type I errors for the restaurant-level effects (Bliese, Maltarich, & Hendricks, 2017). In order to limit the potential for biased results due to multicollinearity, the coworker variables were group-mean centered prior to testing the direct and interaction effects (Aiken & West, 1991). The variance inflation factors (VIFs) ranged from 1.02 for gender to 3.61 for the stage of employment × coworker emotional support interaction term. Given that the VIFs were less than 10, substantial multicollinearity was not present, which might have otherwise biased the coefficients (Cohen et al., 2003). The independent variables were entered into the RCM model in three steps. In Step 1, turnover was regressed on the three control covariates (age, gender, and ethnicity). In Step 2, turnover was regressed on the three controls, stage of employment, and the predictors. Finally, Step 3 included the addition of the interaction terms (stage of employment × abusive coworker treatment, stage of employment × coworker emotional support, and stage of employment × coworker instrumental support).

Results

Table 2 provides the descriptive statistics and correlations among the study variables. The RCM results are presented in Table 3. The R² for the model including only the control variables was .02. The R² for the model that included the control variables plus stage of employment, coworker support, and the abusive coworker treatment predictors was .08. The R² for the full model was .09.

Table 2.

Descriptive Statistics and Correlations.

	M	SD	1	2	3	4	5	6	7	8
1. Turnover	0.32	0.47	—
2. Abusive coworker treatment	1.28	0.25	.01	—
3. Coworker emotional support	3.88	0.82	−.09**	−.47**	—
4. Coworker instrumental support	3.60	0.84	.00	−.41**	.70**	—
5. Age	27.23	7.59	−.18**	.00	−.10**	−.03	—
6. Ethnicity	0.77	0.42	−.04	.00	−.06	−.04	.10**	—
7. Gender	0.68	0.47	.01	−.06	.03	.01	−.04	.07*	—
8. Stage of employment	0.30	0.46	.27**	−.10**	−.03	.12**	−.20**	−.10**	−.04	—

Note. n = 979. Turnover: 1 = leaver and 0 = stayer; ethnicity: 1 = Caucasian and 0 = other; gender: 1 = female and 0 = male; stage of employment: 1 = newcomer and 0 = experienced employee. Correlations with dichotomous variables are point-biserial. Significance levels reflect two-tailed tests.

p < .05. **p < .01.

Table 3.

RCM Predicting Voluntary Turnover.

	Step 1		Step 2		Step 3
	b	SE	b	SE	b	SE
Age	−.01**	.00	−.01**	.00	−.01**	.00
Gender	.00	.03	.02	.03	.02	.03
Ethnicity	−.03*	.04	−.01	.03	−.02	.03
Stage of employment			.23**	.03	.24**	.03
Abusive coworker treatment			.00	.07	.00	.07
Coworker emotional support			−.08**	.03	−.07**	.03
Coworker instrumental support			.03	.02	.03	.02
Stage of employment × abusive coworker treatment					.38*	.15
Stage of employment × coworker emotional support					.02	.05
Stage of employment × coworker instrumental support					.03	.05
~R²	.02		.08		.09

Note. n = 979; k = 78. gender: male = 0 and female = 1; ethnicity: other = 0 and Caucasian = 1; stage of employment: 1 = newcomer and 0 = experienced employee. Significance levels reflect two-tailed tests. ~R² = pseudo R².

p < .05 **p < .01.

Hypothesis 1, which proposed that abusive coworker treatment would be positively related to employee turnover, was not supported. The regression coefficient was nonsignificant for the overall sample (b = .00, p > .10).

Hypothesis 2, which proposed that abusive coworker treatment would exhibit a stronger positive relationship with turnover for newcomers than for more experienced employees, was supported. The regression coefficient for the interaction between stage of employment and abusive coworker treatment was positive and significant (b = .38, p < .05). Figure 1 provides a plot for the interaction between stage of employment and abusive coworker treatment. A simple slope analysis demonstrated that the region of significance for the relationship between abusive coworker treatment and turnover was positive for newcomers (b = .38, z = 2.33, p < .05), but not for experienced employees (b = .00, z = .05, p > .10).

Figure 1.

Interaction between stage of employment and abusive coworker treatment.

Hypothesis 3, which proposed that coworker emotional support would be negatively related to turnover, was supported. The regression coefficient of −.08 was significant at .01 level.

Hypothesis 4, which proposed that coworker instrumental support would be positively related to turnover, was not supported (b = .03, p > .10).

Hypothesis 5, which proposed abusive coworker treatment would exhibit a stronger relationship with turnover than would coworker emotional and instrumental support, was not supported. Only coworker emotional support was significantly related to turnover for the overall sample.

Discussion

The results from this study demonstrate the importance of coworkers in reducing turnover, while providing a more nuanced perspective on the role of abusive coworker treatment and coworker support in combating the perennial turnover challenge in the hospitality industry. It is a long-held belief that excessive turnover in the hospitality industry among entry-level employees is due to the routine nature of the work, limited compensation, and few opportunities for promotion and advancement (Ellingson et al., 2016; Hinkin & Tracey, 2000; Wasmuth & Davis, 1983). Notwithstanding this reality, the present study has demonstrated that quality of employees’ relationships with coworkers also impacts turnover and retention. While coworker support has been demonstrated to influence turnover in other research (Fisher, 1985; Tews et al., 2013), the impact of abusive coworker treatment on turnover has not been quantitatively examined heretofore. In this respect, an important contribution of the present study has been incorporating the negative side of coworker influences into turnover models.

The different pattern of effects of abusive coworker treatment on turnover for newcomers and experienced employees is worth highlighting. Abusive coworker treatment was found to be significantly related to higher turnover among newcomers but not among experienced employees. The fact that abusive coworker treatment was found to adversely affect turnover among newcomers, suggests that employees are particularly sensitive to incivility and maltreatment as they navigate the social landscape of a new workplace. Although abusive coworker treatment was not related to greater turnover among experienced employees, this finding does not necessarily suggest that abusive coworker treatment is benign for more experienced individuals. Abusive coworker treatment could very well relate to other adverse outcomes, such as lower job satisfaction and greater psychological distress.

Our results also underscore the importance of distinguishing between coworker emotional and instrumental support. Coworker emotional support was found to be significantly related to lower turnover but no significant effect was found for coworker instrumental support. The significant effect for coworker emotional support is consistent with previous research (e.g., Fisher, 1985; Tews et al., 2013) and highlights that entry-level employees in the hospitality industry especially value the development of friendship through their work. Regarding coworker instrumental support, a positive relationship with turnover was hypothesized in line with Tews et al.’s (2013) results and drawing on the argument that greater coworker instrumental support might signal a lack of perceived competence and person-job fit. Given that a consistent pattern of results between instrumental support and turnover has yet to emerge across studies, future research would be valuable that more fully examines this relationship. While coworker emotional and instrumental support are positively correlated, they appear to exhibit different relationships with turnover. As such, future research should continue to distinguish between coworker emotional and instrumental support in turnover investigations.

The findings from this study suggest implications for applied practice. Most fundamentally, given the positive relationship between abusive coworker treatment and turnover among newcomers, such treatment should be minimized in the workplace. Doing so may not only benefit the organization but is arguably the correct thing to do ethically. Even though some may dismiss abusive coworker treatment as trivial and inconsequential, the effects of such treatment are likely not so. Thus, supervisors should communicate with employees the importance of fair interpersonal treatment and take corrective action to remedy problems should they occur. Furthermore, employees should be trained on strategies for effective conflict management to help ensure that problems are resolved constructively and not in a hostile and aggressive fashion. In addition, supervisors should model appropriate behavior themselves. Recent research by Hon and Lu (2016) has demonstrated that a trickle-down effect is prevalent such that abusive supervision contributes to employee aggressive behavior. Thus, if supervisors wish employees to treat others with dignity and respect, they should do so themselves. In a more fun and positive light, social activities could promote comradery and bonding among employees and thereby help curb incivility and promote friendships among employees. Work by Tews, Michel, et al. (2014) has found that fun activities have the potential to enhance constituent attachment and subsequent employee retention.

The results from this study should be interpreted in the context of its limitations. The primary limitation of this research is that it focused on one group of employees in a single organizational context, namely servers in a large restaurant chain. Future research would thus be valuable that examines the influence of abusive coworker treatment and support with additional samples of employees to determine the generalizability of the findings from current research. Along the same lines, research with additional samples could help determine when abusive coworker treatment matters more or less in influencing decisions whether to remain with or leave an organization. With respect to gender, it may be that females are more prone to leave as a result of abusive coworker treatment, following that communal attributes, such as being kind and nurturing, more commonly characterize females (Eagly & Karau, 2002). Furthermore, future research could examine how abusive coworker treatment influences turnover among groups of individuals that have historically been subject to discrimination, such as ethnic and sexual minorities and those with disabilities. It should be noted that the independent variables in the regression models examined herein accounted for only a modest percent of variance in turnover. While modest effects are not uncommon in turnover research (Holtom, Mitchell, Lee, & Eberly, 2008), the models examined in this study represent only a small part of the turnover equation. Future research is needed that not only examines variables related to social support and turnover but also other factors as well. Further research is also recommended that moves beyond static scores of predictor variables. Lee, Hom, Eberly, Li, and Mitchell (2017) argue that momentum matters, such that the trajectory of change in predictor scores over time may account for additional variance in turnover. In the context of abusive coworker treatment, it would thus be valuable to examine how increases and decreases of such treatment over time influence decisions to remain with or leave an organization.

In addition to the opportunities discussed above, other research avenues are worth pursuing. One opportunity for future research is examining mediators in the relationship between abusive coworker treatment and turnover to help explain how abusive treatment influences behavior. For example, research would be valuable that focuses on need fulfillment of autonomy, competence, and relatedness in the context of self-determination theory, affective organizational commitment (Meyer, Stanley, Herscovitch, & Topolnytsky, 2002), and organization-based self-esteem (Pierce & Gardner, 2004) in this process. Another opportunity is examining what factors contribute to incidences of abusive coworker treatment. In the context of the Big Five personality dimensions, it may be those with higher levels of agreeableness, conscientiousness, emotional stability who are less apt to engage in abusive coworker treatment, in line with previous research that demonstrated that such individuals engage in less interpersonal deviance (Berry, Ones, & Sackett, 2007). In addition to supervisor support, future research could assess how perceived organizational support (Eisenberger, Stinglhamber, Vandenberghe, Sucharski, & Rhoades, 2002) and specific HRM practices, such as nondiscrimination policies, curb abusive coworker treatment. Another opportunity for future research is to examine the boundaries between fun and maltreatment. In this respect, it would be valuable to assess what types of joking, play, and other forms of interactions employees find to be acceptable or not.

The results from the current investigation have highlighted that better interpersonal relationships are a significant driver of employee turnover and retention. While certainly not the only factor that influence decisions to remain with or leave an organization, the findings from this study highlight that impersonal relationships should not be neglected. It is hoped that this study has provided needed insight into the impact of abusive coworker treatment and coworker support among entry-level employees in the hospitality industry and will stimulate further research in this area.

Footnotes

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) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Michael J. Tews is an associate professor in the School of Hospitality Management at Penn State University. He received his PhD in Human Resource Management from the School of Hotel Administration at Cornell University and his Masters in Industrial Relations from the London School of Economics. His research addresses issues relating to employee selection, training and development, and retention in hospitality and service contexts.

John W. Michel is an associate professor of management in the Sellinger School of Business & Management at Loyola University of Maryland. He received his PhD from the Organizational Studies program at the University at Albany, SUNY. His research examines topics including supportive and fun work environments, leadership, prosocial behavior, and employee selection and retention.

Kathryn Stafford is an associate professor emeritus in the College of Education and Human Ecology at Ohio State University. In addition to her research on human resource management, her research has focused on the management practices of business owning families, which spanned the course of over 25 years. She earned her PhD in Consumer Economics from Cornell University.

References

Aiken

L. S.

West

S. G.

(1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.

Allen

T. D.

McManus

S. E.

Russell

J. E.

(1999). Newcomer socialization and stress: Formal peer relationships as a source of support. Journal of Vocational Behavior, 54, 453-470.

Bauer

T. N.

Bodner

Erdogan

Truxillo

D. M.

Tucker

J. S.

(2007). Newcomer adjustment during organizational socialization: A meta-analytic review of antecedents, outcomes, and methods. Journal of Applied Psychology, 92, 707-721.

Beehr

T. A.

Jex

S. M.

Stacy

B. A.

Murray

M. A.

(2000). Work stressors and coworker support as predictors of individual strain and job performance. Journal of Organizational Behavior, 21, 391-405.

Berry

C. M.

Ones

D. S.

Sackett

P. R.

(2007). Interpersonal deviance, organizational deviance, and their common correlates: A review and meta-analysis. Journal of Applied Psychology, 92, 410-424.

Bliese

P. D.

Maltarich

M. A.

Hendricks

J. L.

(2017). Back to basics with mixed-effects models: Nine take-away points. Journal of Business and Psychology, 33, 1-23.

Bowling

N. A.

Beehr

T. A.

(2006). Workplace harassment from the victim’s perspective: A theoretical model and meta-analysis. Journal of Applied Psychology, 91, 998-1012.

Camerer

C. F.

Loewenstein

Rabin

(2011). Advances in behavioral economics. Princeton, NJ: Princeton University Press.

Carretié

Mercado

Tapia

Hinojosa

J. A.

(2001). Emotion, attention, and the “negativity bias,” studied through event-related potentials. International Journal of Psychophysiology, 41, 75-85.

10.

Chao

G. T.

O’Leary-Kelly

A. M.

Wolf

Klein

H. J.

Gardner

P. D.

(1994). Organizational socialization: Its content and consequences. Journal of Applied Psychology, 79, 730-743.

11.

Cohen

West

S. G.

Aiken

L. S.

(2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

12.

Deci

E. L.

Ryan

R. M.

(2001). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268.

13.

Duke

A. B.

Goodman

J. M.

Treadway

D. C.

Breland

J. W.

(2009). Perceived organizational support as a moderator of emotional labor/outcomes relationships. Journal of Applied Social Psychology, 39, 1013-1034.

14.

Eisenberger

Stinglhamber

Vandenberghe

Sucharski

Rhoades

(2002). Perceived supervisor support: Contributions to perceived organizational support and employee retention. Journal of Applied Psychology, 87, 365-373.

15.

Ellingson

J. E.

Tews

M. J.

Dachner

A. M.

(2016). Constituent attachment and voluntary turnover in low-wage/low-skill service work. Journal of Applied Psychology, 101, 129-140.

16.

Erikson

E. H.

(1968). Identity: Youth and crisis. New York, NY: W. W. Norton.

17.

Felps

Mitchell

T. R.

Hekman

D. R.

Lee

T. W.

Holtom

B. C.

Harman

W. S.

(2009). Turnover contagion: How coworkers’ job embeddedness and job search behaviors influence quitting. Academy of Management Journal, 52, 545-561.

18.

Fisher

C. D.

(1985). Social support and adjustment to work: A longitudinal study. Journal of Management, 11, 39-53.

19.

Gagné

Deci

E. L.

(2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26, 331-362.

20.

Gilovich

Griffin

Kahneman

(2002). Heuristics and biases: The psychology of intuitive judgment. Cambridge, England: Cambridge University Press.

21.

Glomb

T. M.

Tews

M. J.

(2004). Emotional labor: A conceptualization and scale development. Journal of Vocational Behavior, 64, 1-23.

22.

Griffeth

R. W.

Hom

P. W.

Gaertner

(2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26, 463-488.

23.

Harris

K. J.

Harvey

Kacmar

K. M.

(2011). Abusive supervisory reactions to coworker relationship conflict. Leadership Quarterly, 22, 1010-1023.

24.

Herschcovis

M. S.

Barling

(2010). Towards a multi-foci approach to workplace aggression: A meta-analytic review of outcomes from different perpetrators. Journal of Organizational Behavior, 31, 24-44.

25.

Hinkin

T. R.

Tracey

J. B.

(2000). The cost of turnover: Putting a price on the learning curve. Cornell Hotel and Restaurant Administration Quarterly, 41, 14-21.

26.

Hogh

Hoel

Carneiro

I. G.

(2011). Bullying and employee turnover among healthcare workers: A three-wave prospective study. Journal of Nursing Management, 19, 742-751.

27.

Holtom

B. C.

Mitchell

T. R.

Lee

T. W.

Eberly

M. B.

(2008). Turnover and retention research: A glance at the past, a closer review of the present, and a venture into the future. Academy of Management Annals, 2, 231-274.

28.

Hom

P. W.

Griffeth

R. W.

(1995). Employee turnover. Cincinnati, OH: Southwestern College.

29.

Hon

A. H.

(2016). When will the trickle-down effect of abusive supervision be alleviated? The moderating roles of power distance and traditional cultures. Cornell Hospitality Quarterly, 57, 421-433.

30.

Ito

T. A.

Larsen

J. T.

Smith

N. K.

Cacioppo

J. T.

(1998). Negative information weighs more heavily on the brain: The negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75, 887-900.

31.

Iverson

R. D.

(1999). An event history analysis of employee turnover: The case of hospital employees in Australia. Human Resource Management Review, 9, 397-418.

32.

Iverson

R. D.

Pullman

J. A.

(2000). Determinants of voluntary turnover and layoffs in an environment of repeated downsizing following a merger: An event history analysis. Journal of Management, 26, 977-1003.

33.

Kahneman

Slovic

Tversky

(1982). Judgement under uncertainty: Heuristics and biases. Cambridge, England: Cambridge University Press.

34.

Karasek

R. A.

Theorell

(1990). Healthy work. New York, NY: Basic Books.

35.

Karatepe

O. M.

(2012). The effects of coworker and perceived organizational support on hotel employee outcomes: The moderating role of job embeddedness. Journal of Hospitality & Tourism Research, 36, 495-516.

36.

LeBlanc

M. M.

Kelloway

E. K.

(2002). Predictors and outcomes of workplace violence and aggression. Journal of Applied Psychology, 87, 444-453.

37.

Lee

T. W.

Hom

P. W.

Eberly

M. B.

Mitchell

T. R.

(2017). On the next decade of research in voluntary employee turnover. Academy of Management Perspectives, 31, 201-221.

38.

Maertz

C. P.

Campion

M. A.

(2004). Profiles in quitting: Integrating process and content turnover theory. Academy of Management Journal, 47, 566-582.

39.

Maertz

C. P.

Griffeth

R. W.

(2004). Eight motivational forces and voluntary turnover: A theoretical synthesis with implications for research. Journal of Management, 30, 667-683.

40.

Meyer

J. P.

Stanley

D. J.

Herscovitch

Topolnytsky

(2002). Affective, continuance, and normative commitment to the organization: A meta-analysis of antecedents, correlates, and consequences. Journal of Vocational Behavior, 61, 20-52.

41.

Morrison

E. W.

(2002). Newcomers’ relationships: The role of social network ties during socialization. Academy of Management Journal, 45, 1149-1160.

42.

Mossholder

K. W.

Settoon

R. P.

Henagan

S. C.

(2005). A relational perspective on turnover: Examining structural, attitudinal, and behavioral predictors. Academy of Management Journal, 48, 607-618.

43.

Muthén

B. O.

(2017). Mplus user’s guide. Los Angeles, CA: Muthén & Muthén.

44.

Nielsen

M. B.

Einarsen

(2012). Outcomes of exposure to workplace bullying: A meta-analytic review. Work & Stress, 26, 309-332.

45.

Pierce

J. L.

Gardner

D. G.

(2004). Self-esteem within the work and organizational context: A review of the organization-based self-esteem literature. Journal of Management, 30, 591-622.

46.

Pinheiro

J. C.

Bates

D. M.

(2000). Mixed-effects models in S and S-PLUS: New York, NY: Springer-Verlag.

47.

Rozin

Royzman

E. B.

(2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5, 296-320.

48.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68-78.

49.

Ryan

R. M.

Deci

E. L.

(2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York, NY: Guilford Press.

50.

Settoon

R. P.

Mossholder

K. W.

(2002). Relationship quality and relationship context as antecedents of person- and task-focused interpersonal citizenship behavior. Journal of Applied Psychology, 87, 255-267.

51.

Sonnentag

(2003). Recovery, work engagement, and proactive behavior: A new look at the interface between nonwork and work. Journal of Applied Psychology, 88, 518-528.

52.

Spector

P. E.

Jex

S. M.

(1998). Development of four self-report measures of job stressors and strain: Interpersonal conflict at work scale, organizational constraints scale, quantitative workload inventory, and physical symptoms inventory. Journal of Occupational Health Psychology, 4, 356-367.

53.

Tepper

B. J.

(2000). Consequences of abusive supervision. Academy of Management Journal, 43, 178-190.

54.

Tews

M. J.

Michel

J. W.

Allen

D. G.

(2014). Fun and friends: The impact of workplace fun and constituent attachment on turnover in a hospitality context. Human Relations, 67, 923-946.

55.

Tews

M. J.

Michel

J. W.

Ellingson

J. E.

(2013). The impact of coworker support on employee turnover in the hospitality industry. Group and Organization Management, 38, 630-653.

56.

Tews

M. J.

Stafford

Michel

J. W.

(2014). Life happens and people matter: Critical events, constituent attachment, and turnover among part-time hospitality employees. International Journal of Hospitality Management, 38, 99-105.

57.

Thaler

R. H.

(2015). Misbehaving: The making of behavioral economics. New York, NY: W. W. Norton.

58.

Tokuno

(1986). The early adult transition and friendships: Mechanisms of support. Adolescence, 21, 593-606.

59.

Tracey

J. B.

Hinkin

T. R.

(2010). Contextual factors and cost profiles associated with employee turnover. In Enz

(Ed.), The Cornell School of Hotel Administration handbook of applied hospitality strategy (pp. 736-753). Thousand Oaks, CA: Sage.

60.

Tracey

J. B.

Sturman

M. C.

Tews

M. J.

(2007). Ability versus personality: Factors that predict employee job performance. Cornell Hotel and Restaurant Administration Quarterly, 48, 313-322.

61.

U.S. Bureau of Labor Statistics. (2015). Labor force, employment, and unemployment statistics by age group, Labor force statistics from the Current Population Survey. Retrieved from https://www.bls.gov/cps/demographics.htm#age.

62.

Van Dusen

. (2008, March 24). Ten signs you’re being bullied at work. Forbes. Retrieved from https://www.forbes.com/2008/03/22/health-bullying-office-forbeslife-cx_avd_0324health.html

63.

Wasmuth

W. J.

Davis

S. W.

(1983). Managing employee turnover. Cornell Hotel and Restaurant Administration Quarterly, 23, 15-22.

64.

Williams

(2011, May 3). The silent epidemic: Workplace bullying. Psychology Today. Retrieved from https://www.psychologytoday.com/blog/wired-success/201105/the-silent-epidemic-workplace-bullying

65.

Woods

R. H.

Heck

Sciarini

(1998). Turnover and diversity in the lodging industry. Washington, DC: American Hotel Foundation.