DEVIANCE OF DISIDENTIFIED EMPLOYEES?
Master Thesis, Msc Human Resource Management University of Groningen, Faculty of Economics and Business
June, 2014 JUDITH GERRITS Damsterdiep 212 9743 EP Groningen (06) 20796786 judith.gerrits@hotmail.com Student number: 2014254 Supervisor:
Dr. S. Täuber - Human Resource Management & Organizational Behaviour
Abstract:
Because the Dutch economy suffers from huge losses due to destructive deviance, this research analyzes the relationship between organizational disidentification and destructive deviance. It is also investigated whether leader supportiveness has an main effect on destructive deviance and whether leader supportiveness plays a moderating role in the relationship between disidentification and destructive deviance, because there seems to be no consensus on this topic in literature. The relationships are analyzed by using an online questionnaire. Employees of gemeente Stadskanaal (n=80) took part in this survey and after analyzing their responses it was found that there is a negative relationship between perceived leader supportiveness and destructive deviance. No significant relationship between disidentification and destructive deviance was found and leader supportiveness does not play a moderating role in this relationship.
INTRODUCTION AND THEORY
Destructive deviance is a pervasive and expensive problem for organizations (Bennett & Robinson, 2000). It is defined as voluntary behavior that violates significant organizational norms and in doing so, threatens the well-being of an organization, its members, or both (Galperin, 2002). Destructive deviance is also known as workplace deviance, counterproductive behavior, and antisocial behavior (Appelbaum, Iaconi & Matousek, 2007; Bolton, Harvey, Grawith, & Barber, 2012; Laconi & Matousek, 2007). Due to destructive deviance, organizations suffers from huge losses (Robinson & Bennett, 1995). For example, absenteeism, as one form of destructive deviance, causes losses of approximately 12.6 billion euro´s for Dutch economy each year (Elsevier, 12.03.2012). Other forms of destructive deviance will further increase the amount of losses. Destructive deviance can be a result of force majeure, but it can also be a consequence of the work itself (Elsevier, 12.03.2012). If causes of destructive deviance can be found in the work itself, can the problem of huge losses in Dutch economy be reduced by changing aspects of work?
Destructive Deviance
behavior that violates the formally proscribed norms delineating the minimal quality and quantity of work to be accomplished (Robinson & Bennett, 1995). Property deviance is defined as those instances where employees acquire or damage the tangible property or assets of the work organization without authorization (Robinson & Bennett, 1995). Both categories are non-interpersonal and harmful to organizations (Robinson and Bennett, 1995)
Research gives many antecedents for these forms of deviant behavior, ranging from a way to compensate an employee’s underpayment inequity, a need to vent, release, or express one’s feelings of outrage, anger, or frustration and reactions to perceived injustice (Bennett & Robinson, 2000; Greenberg & Scott, 1996; Robinson & Bennett, 1997). All of these causes of destructive deviance have an equal, underlying principle: employees do not agree with a particular aspect of the organization. When employees disagree with their organization, they can feel a sense of separateness from their organization (Bhattacharya & Elsbach, 2002). This sense of separateness can be described as disidentification.
Disidentification
organizational disidentification have to be seen as independent forces that determine how one ultimately relates to an organization (Vadera & Pratt, 2013).
According to Elsbach and Bhattacharya (2001), employees take action when they disidentify from their organization and an action that individuals could take is showing destructive behavior (Vadera & Pratt, 2013). Peloza and Papania (2008) state that when employees disidentify from an organization, their actions can result in significant harm to the firm. Likewise, the findings of Chang, Kuo, Su, and Taylor (2013) reveal that disidentification is related to increased workplace deviance.
Despite of these findings, disidentification and its effects did not attract a lot of research attention yet (Chang et al., 2013). Besides, Bolton and colleagues (2012) state that there is currently no research regarding the effects of organizational disidentification on counter-productive work behaviors. Therefore, it would be theoretically relevant to investigate this subject and to analyze the relation between disidentification and destructive behavior. Assuming that disidentification is the underlying force of many causes of destructive deviance, this paper researches the relationship between disidentification and destructive behavior.
H1: Disidentification is positively related to destructive deviance.
Leader Supportiveness
determine factors that might buffer against the negative consequences of disidentification on work-related behavior. In this way, it can be determined whether the negative consequences of this relationship can be reduced.
The management of negative deviant behavior in the workplace is of growing concern in organizations globally since such behaviors can be detrimental to their financial well-being (Appelbaum et al., 2007). In the literature, numerous forms of management are discussed. The present study will research the effect of leader supportiveness, because this is a variable which can be applied relatively easy. This is because perceived leader support is the feature that is under the most direct control of the immediate supervisor (Amabile, Schatzel, Moneta & Kramer, 2004). Supportive leaders are defined as leaders that show interest in their employees’ well-being, personal growth, and development and according to Maier (1958), examples of supportive leadership behavior are helping, coaching and counseling subordinates. Being supportive is one of the tasks of a leader (Quinn & Rohrbaugh, 1983). In their competing values framework, Quinn and Rohrbaugh (1983) described the roles of managers and leaders and one of these roles is being a mentor, which consists of engaging in the development of people through a caring, empathetic orientation. In this role the leader must be helpful, considerate, sensitive, approachable, open, and fair (O'Neill & Quinn, 1993).
Most research is focused on the direct relationship between leader supportiveness and destructive deviance. Because there does not seem to be consensus in the literature, this relationship will be analyzed in the present research. Besides, leader supportiveness will be analyzed as possible moderator between disidentification and destructive deviance. This will be theoretically relevant, because it will be examined whether leader support functions as a buffer against the negative consequences of disidentification.
In his research, Hirschi (1969) found a relationship between leader supportiveness and destructive deviance. He states that when employees feel that they are unable to get support from their manager, these employees will be more likely to break the rules and engage in destructively deviant behavior.
However, Galperin (2002) and Abbey and Boon (2011) did not find this relationship. In her research, Galperin (2002) investigated whether employees who perceive high leader supportiveness will engage in less destructive deviance. She did not find a significant relationship between leadership supportiveness and destructive deviance. Abbey and Boon (2011) investigated whether perceived supervisor support could result in additional commitment to the organization which could lead to an increase in behaviors that benefit the organization in general. Analyzing this relationship did not result in a significant outcome.
Because there does not seem to be consensus in the literature, the effect of leader supportiveness on destructive deviance is in the focus of the present research. It would be theoretically relevant to research the relationship between these variables, in order to find more support for one of the two streams in the literature and to provide conclusive evidence regarding the impact of leader supportiveness on destructive behavior. This way, insights can be gained as to whether leader supportiveness might reduce the destructive behavior that employees can show. From an applies perspective, this would inform leaders about their impact on employees’ destructive behavior and whether they can alter this.
H2: Leader supportiveness is negatively related to destructive deviance.
Leader Supportiveness as Moderator
The term ‘support’ typically refers to relationship-oriented behaviors, which focus on the socioemotional: showing consideration for subordinates’ feelings, acting friendly and personally supportive to them, and being concerned for their welfare (Amabile et al., 2004). When supervisors show these behaviors, disidentified employees could feel that their supervisors understand them, which could affect their behavior.
Ashour and Johns (1983) state that leader supportiveness influences employees’ behavior. In their research, they found that valuable, positive incentives of a leader facilitate behavior acquisition by subordinates. The greater the leader’s use of positive incentives and successive approximation procedures, the higher will be the rate of subordinate’s behavior acquisition (Ashour & Johns, 1983). This means that employees will copy the behavior of their supervisors, which results in positive behavior of subordinates that perceive positive, supportive behavior of their leaders.
Rhoades and Eisenberger (2002) found a relation between employees’ perceived support and their mood. According to them, support has a strong, positive relationship with the positive mood of employees. An individuals’ mood influences his or her behavior (Gardner, 1985). Therefore, the positive effect of leader supportiveness on employees’ mood might affect the behavior of disidentified employees and reduce the possible negative behavior they would show because of their disidentification.
supportiveness can change the destructive behavior that individuals might show when they feel disidentified.
H3: Leader supportiveness moderates the relationship between
disidentification and destructive behavior. When leader supportiveness is perceived as low,
the positive relationship between disidentification and destructive deviance is stronger than
when leader supportiveness is perceived as high.
In sum, the present research examines whether there is a relationship between disidentification and destructive deviance (hypothesis 1), and whether this relationship is moderated by leader supportiveness (hypothesis 3). Finally, tying in with prior research, the direct effects of leader supportiveness on destructive deviance (hypothesis 2) are examined. The hypothesized model is shown in figure 1.
Figure 1.
Hypothesized model
METHODOLOGY
This section describes the methods used to conduct the study. The type of methodology chosen is an empirical study via an online questionnaire. The online questionnaire is appropriate for this study because it is an unobtrusive and economic way to collect data from a large population (Abbey & Boon, 2011).
Leader Supportiveness
Participants and Procedure
To test my hypotheses, 256 employees of gemeente Stadskanaal were invited to take part in the study. An online survey was sent to all members of gemeente Stadskanaal in March 2014 and 80 of them actually participated in this study. This makes the response rate nearly one third of the employees (31%). 39 respondents were women, 29 were men (12 missing values). The mean age of the respondents was 48.52 years (SD = 10.46).
The participants received an online survey on their company e-mail accounts, which they could fill in anonymous. The survey took them about 10 minutes to fill in and they did not get any form of compensation. Participation was voluntary and participants were not informed about the survey beforehand.
Research Design and Measures
This survey was taken in the form of an online questionnaire. To measure the variables, various existing scales were used, which all used five-point Likert-type items (1 = strongly disagree; 5 = strongly agree). Appendix I shows the items of all measure scales.
To measure disidentification, the scale of Täuber, Sassenberg and Van Der Vegt (in preparation) was used, consisting of 12 items. To see whether these 12 items form a realistic scale, the Cronbach’s Alpha was computed. The Cronbach’s Alpha had to be higher than 0.7 to be sure that the items were internally consistent and this was the case (α = 0.83).
To measure the dependent variable destructive deviance, Bennett and Robinson’s scale (2000) was used. This scale consists of 12 items, measured at an five-point scale (1 = never, 5 = daily). To see whether these 12 items form a realistic scale, the Cronbach’s Alpha was computed. The Cronbach’s Alpha had to be higher than 0.7 to be sure that the items were internally consistent and this was the case (α = 0.91).
Because all CA’s were above 0.7, the items of each construct were computed into one variable. This resulted in three sum variables, representing the constructs disidentification, leader supportiveness and destructive deviance respectively.
Control Variables
Following Dyne and LePine (1998) the control variables age and sex were included in the analyses. Respondents were also asked if they were in a leading position or not, how long they had been working in their lives and how many hours they worked for gemeente Stadskanaal in an average week. Five respondents were in a leading positions, 65 respondents were not (10 missing values). On average, the respondents were working for 26.86 years (SD = 11.03) and their mean tenure with gemeente Stadskanaal was 16.75 years (SD = 11.43). The respondents worked 32.14 hours a week (SD = 7.03) on average. These control variables were included in the analyses, to measure whether they affect the research question.
Data Analysis
To test my hypothesis, a correlation analysis and a regression analysis are conducted. The next section will show the results of these analyses.
This section describes the results of the analyses. I used an alpha level of .05 for all statistical tests.
Descriptive Analysis
First of all, the correlations between the variables were tested. The linear associations between the variables disidentification, leader supportiveness and destructive deviance, and the control variables were measured. Table 1 displays the correlations among the measured variables.
Table 1
Means, Standard Deviations and Pearson Correlations
Note: *p<.05
First, the correlation between disidentification and destructive deviance was analyzed. In contrast to what was expected, no significant correlation between disidentification and destructive deviance was found, r(71) = -.03, p > .05. Second, the correlation between disidentification and leader supportiveness was analyzed. In contrast to what was expected, no significant correlation between disidentification and leader supportiveness was found, r(77) = -.15, p > .05. Third, the correlation between leader supportiveness and destructive deviance was analyzed. In contrast to what was expected, no significant correlation between disidentification and destructive deviance was found r(71) = -.13, p > .05.
From the control variables, sex was the only variable which had a significant correlation. This correlation was found between sex and leader supportiveness, r(68) = .31, p <.05. Sex will be taken into account when conducting further analyses, because relations between sex and the relevant research variables are found.
Regression Analysis
A linear regression was conducted to investigate the effect of disidentification and leader supportiveness on destructive behavior. First, the variables were standardized and an interaction was created. Subsequently, a linear regression analysis was conducted (R² =.09,
F=(4, 63)=1.51, p=.21).
No main effect was found from disidentification on destructive deviance, (B =-.08,
t(67)=-.65, p = n.s). A main effect was found in the expected direction from leader
supportiveness on destructive deviance, (B =-.29, t(67)=-2.02, p < .05), however, caution is required, because the regression model was not significant. No interaction effect between leader supportiveness and disidentification on destructive deviance was found (B =.19,
t(67)=1.47, p = n.s.), meaning that leader supportiveness does not moderate the relationship
between disidentification and destructive deviance.
DISCUSSION AND CONCLUSION
In this research the main effects of disidentification and leader supportiveness on destructive deviance were investigated. It was also investigated whether leader supportiveness plays a moderating role in the relationship between disidentification and destructive deviance. It was expected that disidentification was positively related to destructive deviance (hypothesis 1), that leader supportiveness was negatively related to destructive deviance (hypothesis 2) and that leader supportiveness moderates the relationship between disidentification and destructive deviance (hypothesis 3). This means that when leader supportiveness is perceived as weak, the relationship between disidentification and destructive deviance becomes stronger.
to what was expected, no significant relationship was found between disidentification and destructive deviance (hypothesis 1), and this relationship was not moderated by leader supportiveness (hypotheses 3).
Testing the direct relation between leader supportiveness and destructive deviance (hypothesis 2) did result in a negative, significant relation. This means that the more leader supportiveness employees receive, the less destructive deviance they perceive.
The fact that not all expectations did get confirmed may be due to several factors. It could be the case that the research method used was not sufficient, or that the questionnaire was not comprehensive and accurate enough. However, there are also some factors in literature that might explain these findings.
Theoretical Implications
Disidentification and destructive deviance. As stated earlier, there is evidence that
there is a positive relation between disidentification and destructive deviance (Chang et al., 2013; Elsbach & Bhattacharya, 2001; Peloza & Papania, 2008; Vadera & Pratt, 2013). However, in the present research, this relationship was not found.
case, they might not notice the destructive deviant behavior of colleagues because of their emotional withdrawal. As a consequence, their response to the destructive deviance questions can be biased, because destructive deviance could be present, but stay unnoticed by the strongly disidentified employees.
In this research, the scale of Bennett and Robinson (2000) was used to overcome the risks of response biases from using self-assessment scales (Adams, Soumerai, Lomas, & Ross-Degnan, 1999), because destructive deviance may be perceived as a sensitive subject. However, assessing the destructive behavior of peers can also bias participants’ responses. Peer rating increases the likelihood of the halo effect (Holzbach, 1978; Tsui & Barry, 1986), which occurs when a rater does not differentiate among distinct items or dimensions in his evaluation of the rate, but evaluates the ratee according to a global or overall judgment. This means that the participants in this research may have responded to the destructive deviance questions in a more positive way, because they like their colleagues. In this case, even strongly disidentified employees might have given denying answers to the destructive deviance questions, making it hard to find a positive relation between disidentification and destructive deviance.
Leader supportiveness and destructive deviance. There is no congruence in literature
about the direct relationship between leader supportiveness and destructive deviance. Hirschi (1969) demonstrated a relation between leader supportiveness and destructive deviance in his research. Galperin (2002) and Abbey and Boon (2011) did not find this relationship.
In this research, a negative relationship was found between leader supportiveness and destructive deviance. This means that when employees get more leader supportiveness, they will perceive less destructive deviance. However, this results requires caution, because the regression model was not significant.
The fact that the model was not significant, could be a consequence of the small sample size of this study. A small sample size might introduce bias into survey results (Templeton, Deehan, Taylor, Drummond & Strang, 1997; Wishman & McClelland, 2005). This point will be discussed in more detail in the limitations section.
Notwithstanding, the relationship found between leader supportiveness and destructive deviance is in line with the findings of Hirschi (1969) and makes a theoretically relevant contribution, by supporting one of the two streams in literature. This might bring literature on this topic one step further in reaching consensus.
Leader supportiveness as moderator. Leader supportiveness could change the
behavior of employees in positive ways (Ashour & Johns, 1983; Rhoades & Eisenberger, 2002), which might change the effect of disidentification on destructive deviance. In this research, no moderating effect of leader supportiveness on the relation between disidentification and destructive deviance was found.
physical. When employees are strongly disidentified, and withdraw emotionally from their organization, it might be that leader supportiveness cannot influence their behavior. This could have been the case for some of the respondents in this research.
The theory of Kerr and Jermier (1978) could also explain why leader supportiveness did not affect the relationship between disidentification and destructive deviance. Their theory demonstrates that certain individual, task, and organizational variables might act as substitutes for leadership, negating the hierarchical superior's ability to exert either positive or negative influence over subordinate attitudes and effectiveness. According to this theory, there might be some variables that can decrease or even withhold the effects of leader supportiveness on the relation between disidentification and destructive deviance and this might have been the case in this research.
Practical Implications
My findings have several practical implications that are important for employers and managers to keep in mind. First of all it was found that leader supportiveness results in less destructive deviance. It is therefore recommended to supervisors to be as supportive as possible. Besides the positive effects of reducing destructive deviance, leader supportiveness can lead to other advantages. It could enhance creativity (Amabile, Schatzel, Moneta, & Kramer, 2004), increase job satisfaction (Kim, Lee, & sung, 2013) and reduce the negative consequences of work-related stressors (Somech & Drach-Zahavy, 2013). Because of the many advantages of leader supportiveness, it is recommended for supervisors to be as supportive as possible to all employees.
are highlighted. The first recommendation concerns person-organization fit. Person-organization fit refers to the compatibility between a person and the Person-organization, emphasizing the extent to which a person and the organization share similar values, norms, and meet each other’s expectation (Chang et al., 2013). The best way to avoid destructive deviance because of disidentification is to avoid disidentified employees (Bolton et al., 2012). This has to be kept in mind in the hiring process of an organization. To overcome disidentification among employees, recruiters and HR managers have to make sure that the employees that they hire will have a good person-organization fit. This will enhance commitment and reduce the chance that employees disidentify from their organization (Chang et al., 2013).
A second recommendation concerns dealing with the current disidentified employees. A solution to manage these employees is to create positive justice perceptions (Galperin, 2002). If employees’ perception of justice increases, they will show less destructive deviance. Organizations can increase employees’ perception of fairness by creating a fair work environment. Such an environment can be created by ensuring employees that there are fair procedures, equitable outcome distributions, and that they are treated with respect (Galperin, 2002).
Limitations
This research has some limitations which have to be kept in mind when interpreting the results. The main limitation of this research is its small sample size. Despite of the fact that the response rate of my questionnaire was extraordinary high for an online survey, the answers of only 80 respondents could be used during the analysis. A small sample like this can introduce bias into survey results (Templeton et al., 1997; Wishman & McClelland, 2005). Therefore, the results of this study cannot be generalized or applied directly to other organizations.
A second limitation could be the use of an online questionnaire. Although online questionnaires have many benefits like time- and cost-savings (Wright, 2005), they also have some disadvantages. Security and data integrity present potential problems. Individuals may harbor suspicions about online survey administration and may have concerns about confidentiality that discourage participation (Sax, Gilmartin, & Bryant, 2003).
A third limitation could be the scale used to measure destructive deviance. The used scale of Bennett and Robinson (2000) did measure perceived destructive deviance. Asking respondents about other’s behavior, rather than their own is called key informant sampling and is used to overcome response biases (Heckathorn, 1997). This method reduces the tendency to exaggerate socially acceptable behavior and understate disreputable behavior, however, it adds several sources of bias. Key informants may lack sufficiently detailed knowledge, the method cannot be used to access highly detailed and personal information and the sampling may have an institutional bias (Heckathorn, 1997).
Future Research
self-ratings and peer-self-ratings of destructive deviance. When using multiple sources of data, one is able to detect the effects of common method variance (Galperin, 2002).
A new scale to analyze destructive deviance may help to find the positive relation between disidentification and destructive deviance, as described in literature (Chang et al., 2013; Elsbach & Bhattacharya, 2001; Peloza & Papania, 2008; Vadera & Pratt, 2013;).
Also, a larger amount of respondents is needed, because the small sample size of this research can introduce bias into survey results (Templeton et al., 1997; Wishman & McClelland, 2005). Although the response rate of this research was quite high (31%), the general assumption is that the higher the response rate the lower the potential of nonresponse error and therefore the better the survey (Dillman, 1991). Future research should thus be done in a larger organization, allowing researchers to invite more employees to take part in the survey, or the response rate should be further increased by using prior notice, follow-up mailings and financial incentives (Dillman, 1991).
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Items of the scales used.
Disidentification (Täuber, Sassenberg en Van der Vegt, in preparation)
Behavioural intentions regarding physically exiting the group
1. I doubt that I will remain in this group for long. 2. I am thinking about leaving this group.
3. I am thinking about backing out of this group. 4. I will definitely leave this group.
5. I will leave this group as soon as possible.
6. I will definitely remain a member of this group. (reverse coded)
Cognitive exit from the group withdrawal
1. I come to the decision to only do what is essential for me as a member of this group. 2. I attend my tasks in the group in such a way that my potential is not fully exhausted. 3. I reduce my load by diminishing my commitment to the group.
4. I do what I have to do in the group, but without great dedication.
Satisfaction with group membership
1. Being a member of this group satisfies me.
2. Spending time with this group dissatisfies me. (reverse coded)
Destructive deviance (Bennet & Robinson, 2000)
1. Taken property from work without permission
2. Spent too much time fantasizing or daydreaming instead of working
3. Falsified a receipt to get reimbursed for more money than you spent on business expenses
5. Come in late to work without permission 6. Littered your work environment
7. Neglected to follow your boss's instructions
8. Intentionally worked slower than you could have worked
9. Discussed confidential company information with an unauthorized person 10. Used an illegal drug or consumed alcohol on the job
11. Put little effort into your work
12. Dragged out work in order to get overtime
Leader supportiveness (Graen, Novak & Sommerkamp, 1982)
1. I usually know where I stand with my manager
2. My manager has enough confidence in me that he/she would defend and justify my decisions if I was not present to do so
3. My working relationship with my manager is effective 4. My manager understands my problems and needs
5. I can count on my manager to ‘bail me out’, even at his or her own expense, when I really need it
6. My manager recognizes my potential
