Abstract
Purpose
The purpose of this paper is to examine the association between mobbing and service innovation performance. In this context, the mediating role of boreout, a new concept in the literature, was examined.
Design/methodology/approach
This study was conducted with 240 participants recruited in manufacturing companies affiliated with Adana Chamber of Industry in the province of Adana Turkey in November 2019. The research was analyzed by the structural equation modeling method with the social exchange theory basis.
Findings
Findings revealed that boreout and mobbing were negatively associated with service innovation performance. Mobbing was positively associated with boreout and job boredom. According to the finding of this study, boreout partially mediated the effect of the mobbing on service innovation performance.
Originality/value
This study reveals the association among mobbing, boreout and service innovation performances of employees of companies operating in manufacturing sector. The findings of this study provide important practical knowledge to businesses and academics regarding the field of management, entrepreneurship and innovation.
Keywords
Citation
Özsungur, F. (2020), "The mediating role of boreout in the effects of mobbing on service innovation performance", Asia Pacific Journal of Innovation and Entrepreneurship, Vol. 14 No. 2, pp. 203-213. https://doi.org/10.1108/APJIE-12-2019-0085
Publisher
:Emerald Publishing Limited
Copyright © 2020, Fahri Özsungur.
License
Published in Asia Pacific Journal of Innovation and Entrepreneurship. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Today’s developing economy differentiates consumer needs and the number of businesses is increasing to meet these needs. The number of employees is also increasing to meet increasing consumers’ differentiated needs (Hyatt et al., 2014). The number of employees created an important human resource management problem for businesses thus the employment problems arose.
Employment is an important factor affecting the innovative behaviors of the employees in the workplace and the performance of the enterprises (Carmeli et al., 2006; Basu and Green, 1997). The workplace is an environment where employment is efficient and functional, where employees work in the same environment, interact with managers, serve customers and express their creative and innovative ideas (Ouedraogo and Koffi, 2018). In this environment, the protection of the psychology of the employees has an impact on the total performance of the enterprise/organization. Mobbing, the crisis of growth and meaning at work, and job boredom are important factors that can affect the innovative behavior of employees (Duffy and Sperry, 2011). Boreout is an up-to-date and psychological concept developed by Stock (2015) that includes these factors. The relationship of these factors with employee service innovation behavior and new services development is among the noteworthy topics. The service innovation behaviors of the employees working in the manufacturing sector and the performance of the company depending on this behavior will contribute significantly to the literature of innovation and entrepreneurship.
This study aims to reveal the association among boreout, mobbing and service innovation performance. The secondary aim of this study is to examine the mediating effect of boreout on this relationship. With the knowledge to be obtained, significant contributions will be provided to management, business, psychology, innovation and entrepreneurship literature.
Literature review and hypotheses development
Mobbing
Mobbing is an emerged concept for managing scarce resources in the labor force need that arises with industrialization for the employees. Conflicts in leader–employee and employee–executive relationships have increased the importance of the concept of mobbing. Studies have shown that mobbing is related to hostile behaviors and harassment (Leymann, 1996), psychological stress and decreased productivity (Vega and Comer, 2005), oppressive, insulting and threatening behaviors that negatively affect employees’ psychology (Agervold and Mikkelsen, 2004). These studies reveal the relationship between mobbing and negative psychological states:
Mobbing is positively associated with boreout.
Mobbing is negatively associated with service innovation performance.
Mobbing is positively associated with the crisis of meaning at work.
Mobbing is positively associated with job boredom.
Mobbing is positively associated with the crisis of growth.
Service innovation performance
Service innovation is an important factor for an organization to gain a sustainable competitive advantage. Increasing and differentiating consumer needs with the development of industry require businesses to focus on service innovation (Woodruff, 1997). This type of performance is related to the service offered by the employees. Therefore, individual perception and psychological state can prevent the creative and innovative ideas and behaviors of the employees (Schmit and Allscheid, 1995). Besides, climate and teamwork within the organization have been identified as other influencing factors on service innovation performance (Hussain et al., 2016):
Mobbing is negatively associated with employee service innovation behavior.
Mobbing is negatively associated with new services development.
Boreout syndrome
Boreout is associated with the crisis of meaning at work, crisis of growth and job boredom states of employees. This negative psychological state has negative effects on the work of the employees. There are a limited number of studies on boreout in the literature. Studies show that boreout has a negative effect on employees’ creative actions (Stock, 2015; Stock, 2016):
Boreout is negatively associated with service innovation performance.
The fact that boreout is a negative psychological state shows that it can have an impact on employees’ performance-based behavior. Stock (2015) revealed that the crisis of growth and meaning at work which were the dimensions of boreout were negatively related to employee innovative work behavior. According to another study, a negative correlation was found between work engagement and burnout (Moeller et al., 2018). This limited number of studies in the literature cannot explain the associations among service innovation, boreout and mobbing:
Boreout mediates the association between mobbing and service innovation performance.
The study model is presented in Figure 1.
Methods
Data collection and sample selection
The universe of this study was determined as employees working in the manufacturing sector in Adana. Accordingly, based on information received from Adana Chamber of Industry in Turkey as of 2019, the number of companies in this sector was 1,614 in Adana. The error level of the selected sample is 5% at a 90% reliability level. Participants (N = 240) were recruited in 98 manufacturing companies those randomly selected and affiliated to the Adana Chamber of Industry in the province of Adana Turkey in November 2019. Participants were selected according to the following criteria: working in the manufacturing industry; and working in a service department. Adana Chamber of Industry and the participants approved the study procedures. The study was conducted with three interviewers in Turkish. In total, 86 people refused to participate in the survey. Participants were informed clearly that they were willing to participate in the survey and that they could end the process at every stage of the survey.
Information on demographic variables is shown in Table 1.
In total, 40% of the participants were female and 60% were male. In total, 52.92% of the participants were between the ages of 27 and 44. In total, 78.75% of the participants were married, and 59.17% were graduated from primary school. The participants were asked about the question of “Which of the following affects your psychology more negatively than the other?” In total, 75.42% of the participants (n = 181) responded as “Burnout due to the workload on you” and 24.58% (n = 59) of the participants responded as “You are bored because of the meaningless or low job/task.”
Measurement
The boreout scale used in the research was developed by Stock (2015). This scale consists of 11 items and three dimensions (crisis of meaning at work [CMW]; job boredom [JB]; and crisis of growth [CG]). The service innovation performance (SIP) scale was developed by Hu et al. (2009). Employee service innovation behavior (ESIB) (six items) includes the new services development of (NSD) (eight items). The workplace mobbing was measured using a five-item scale labeled Luxembourg Workplace Mobbing Scale (WM) developed by Steffgen et al. (2016). Four items were added from the Leymann Inventory of Psychological Terror to the scale (Leymann, 1996).
Data analysis
The structural equation modeling was applied to verify the data and test the model structure and hypotheses. Confirmatory factor analysis was applied using AMOS software to measure the validity and reliability of the measurement tools (Gefen et al., 2000).
Empirical results
Confirmatory factor analysis
The construct validity of the scales was determined via confirmatory factor analysis (CFA). The latent variable is measured by the observed variable in this type of analysis. According to the CFA analysis results, the initial good fit values of the model were as follows, and they were below the threshold values: [χ2 (240): 2,493.469, p < 0.01; χ2/df: 6.203; SRMR: 0.565; RMSEA: 0.149; NNFI: 0.529; IFI: 0.568; CFI: 0.565; GFI: 0.475; AGFI: 0.392]. Two paths are suggested in the literature for the model to meet the good fit criteria. The first is to discard items with low standardized regression weight from the model (Field, 2005; Stevens, 1992). The second path is to link items with high covariance loads (Brown, 2015). However, discarding items from the model is not generally recommended (Brown, 2015). Because when each item is removed, important doubts arise about the reliability of the measuring tool. For this reason, first of all, items with high covariance loads were linked to each other. The reason for this is that the tools that measure negative psychological states create sensitivity to the responses of individuals. Finally, CMW1 and CMW2, NSD2 and NSD3, and NSD7 and NSD8 were linked with covariance. Thus, the model met the good fit criteria. The modified model good fit values are presented in Table 2 [χ2 (240): 759.609, p < 0.01; χ2/df: 1.928; SRMR: 0.036; RMSEA: 0.063; NNFI: 0.916; IFI: 0.925; CFI: 0.924; GFI: 0.826; and AGFI: 0.795].
According to the CFA analysis findings, GFI and AGFI values were found to be below the threshold values. This is because these coefficients are sensitive to the number of samples (Hu and Bentler, 1999). These values are acceptable considering the low sample size of this study and that the research is related to psychological effects. Besides, if the IFI and CFI values are above the threshold values in the literature, it is accepted that the GFI and AGFI values can be low (Hu and Bentler, 1999).
During the model test, latent variables were tested by factor analysis. Whether there is a bias or perceptual effect in the responses of individuals to the survey questions in behavior and psychological state studies affects the results of the research. The estimated results should be determined by the common method bias method whether it is subject to problems caused by measurement error (MacKenzie and Podsakoff, 2012; Podsakoff and Organ, 1986). To eliminate the risk of this error, the following methods were applied: data of dependent or independent variables were collected from different sources and participants (Williams et al., 2010). While designing the questionnaire, the items of each variable were prepared in separate sections. The survey studies were carried out in a suitable environment in the workplace. Besides, information about privacy and security about the study was conveyed to the participants. On the other hand, the common method bias was determined by the single factor method. First of all, all the items of the questionnaire were created under a single structure. Then the single model created and the estimated model were compared. According to the findings obtained, it was determined that there was no common method bias in the measurement tools (Δχ2 = 2,626.12; p < 0.01).
The factor loads of the scales are between 0.66 and 0.99. The indicator variables that a structure manages to explain are determined by the average variance extracted (AVE) and the average amount of variance (Tabachnick and Fidell, 2007). Therefore, the AVE values of the variables of the model were determined as follows: boreout (0.512), mobbing (0.541) and SIP (0.539). These values are above the threshold value recommended in the literature for AVE. On the other hand, Cornbach’s alpha and composit reliability values were determined as follows: boreout (α: 0.837; CR: 0.701), mobbing (α: 0.854; CR: 0.854) and SIP (α: 0.920; CR: 0.767) (Table 3). These results showed that the model had convergent and discriminant validity (Kline, 2016; Fornell and Larcker, 1981; Hair et al., 2016; Nunnally, 1976).
The discriminant validity requires that AVE values for latent variables be lower than the square root of AVE values (Fornell and Larcker, 1981). When Table 3 is examined, it is seen that the AVE value for each variable is lower than the square root of it. The square root of AVE values were determined as follows: boreout (0.715), mobbing (0.736) and SIP (0.734).
Mediation analysis
Path analysis and mediation analysis methods were applied in the study via IBM SPSS AMOS software. Over 80% of the explained variance is the evidence for full mediation. The ratio between 20% and 80% is evidence for partial mediation. The ratio below 20% proves that there is no mediating effect in the model (Hayes, 2013). In the model, the effects of mobbing (exogenous variable) on SIP (endogenous variable) and boreout (endogenous variable) were found to be significant as a result of the analyses (Table 5). As these effects were determined to be significant, the bootstrap method was applied for mediation analysis via IBM SPSS AMOS software (Hair et al., 2016). The Bootstrap method is an important method that provides the detection of indirect effects and is recommended in the literature (Hair et al., 2016).
According to the findings, it was found that boreout had a 27.6% partial mediating effect on the mobbing–SIP relationship. The error variance of boreout was 72.4%. Thus, H9 was supported. The results for the mediation analysis are shown in Table 4. The mediation model including standardized estimates is shown in Figure 2.
Results of hypotheses
According to findings mobbing was negatively associated with SIP (H2: β = −0.436 t = −5.602; p < 0.01), mobbing was positively associated with job boredom (H5: β = 0.449; t = 3.621; p < 0.01) and boreout (H1: β = 0.495; t = 6.039; p < 0.01) and boreout was negatively associated with SIP (H3: β = −0.139; t = −2.407; p < 0.05). Research results showed that H1, H2, H3 and H5 hypotheses related to direct effects were supported. On the other hand, H4, H6, H7 and H7 were rejected. These results did not support that mobbing had a direct effect on CMW, CG, ESIB and NSD (Table 5).
Discussion
Service innovation performance is a criterion put forward by an organization in its innovative actions. Innovative actions of employees are influenced by many factors such as organizational climate, psychological states of employees and leader-employee interaction. The emergence of these effects is possible only by determining the psychological state of the individual. This study was carried out to determine the effects of mobbing, which is one of the current issues of today, on the service innovation performance of the enterprise and the mediating role of boreout syndrome on these effects.
According to the findings of this study, mobbing has a significant effect on boreout. There is no empirical study in the literature that reveals this relationship. On the other hand, studies in the literature regarding mobbing are related to bullying (Harper, 2020) and psychosocial risk factors in service companies (Marín and Piñeros, 2019).
According to the findings of this study, mobbing and boreout have a negative impact on the service innovation performance of companies operating in the manufacturing sector. These results reveal the importance of negative psychological effects such as mobbing and boreout in the innovation performance of the business. Stock (2015) found that the crisis of meaning at work and crisis of growth had negative effects on employees’ innovative behavior. Stock (2016) revealed the negative effect of the crisis of meaning at work, job boredom and crisis of growth on customer-oriented behavior. Moeller et al. (2018) detected a negative interindividual correlation between burnout and work engagement. However, these findings are not sufficient to explain the findings of our study. Because service innovation performance consists of the development of new services and employee service innovation behavior.
Another remarkable result in the research findings is the positive effect of mobbing on job boredom. Job boredom can occur in two ways as more or less workload. Mobbing, on the other hand, is the intimidation, bullying behaviors of the employer or executives on the employee. These behaviors are carried out by little workload, disregard, over workload and fatigue of the employee. Therefore, it is possible to say that both concepts are related. In addition, this finding reveals the importance of job boredom in the negative effect of mobbing on SIP.
Studies regarding service innovation performance in the literature are related to close cooperation with customers (Santamaría, Nieto and Miles, 2012), intentions and behaviors (Schmit and Allscheid, 1995), team culture and knowledge sharing (Hussain et al., 2016). The common point of these studies reveals that negative psychological effects have a negative effect on the employees. On the other hand, the mediator effect of boreout has not been examined in the literature. According to the findings of this study, boreout mediates the relationship between mobbing and SIP. The reason for this is that negative events affecting employees affect their psychology, and because of these effects, they limit their creativity and innovative actions. Boreout, which plays an explanatory role in mobbing’s impact on SIP, negatively affects employees’ contribution to the innovation performance of the business. On the other hand, the direct effects of mobbing on CMW, CG, ESIB and NSD could not be detected. These findings reveal the importance of the combined effects of all dimensions in the effect of mobbing on SIP. However, the same does not apply to the effect of mobbing on boreout. Because there is a positive and significant association between mobbing and JB.
Conclusion
The developing technology and the increasing workload affect the psychological levels and innovative behaviors of the employees negatively. Increasing quality standards, supply, future customer expectations, new markets and rivalry significantly affect the performance of businesses. In this context, mobbing is one of the current and important problems of businesses and organizations. Businesses develop strategies to increase their total performance and increase their competitiveness by demonstrating innovation in the services of their employees. From this point, the outputs of this study reveal the associations among mobbing, boreout and service innovation performance. Although there are no studies on this subject in the literature, this study has some limitations.
The sample constitutes the limitation of this study. Performing the study with the sample of manufacturing companies transforms a target audience to a limited sample. Therefore, it is recommended to conduct researches on a sample with different demographic features for future studies. On the other hand, the study of negative psychological situations such as boreout and mobbing on employees operating in the manufacturing sector makes it difficult to conduct the study. Many people did not accept to participate in the survey.
The new knowledge that this study adds to the literature is the determination of the mediation effect of boreout in the relationship between mobbing and service innovation performance. For future studies, it is recommended to examine why mobbing is not effective on CMW, CG, ESIB and NSD by qualitative method. Boreout’s partial mediating effect shows that different factors may be effective in the mobbing–SIP relationship. Research is also required to determine these factors.
The effects of boreout and mobbing on SIP require that the human resources policies of the companies in the manufacturing sector be developed to increase the creativity and innovative actions of the employees. Identifying psychological effects such as mobbing and boreout that prevent employees from developing their innovative actions in service delivery is important in the context of business performance. It should be taken into consideration by executives that the employees who faced with the job boredom effect may have been exposed to mobbing. The psychological health of the employees in the workplace can be protected with a strategy to be implied in this direction.
Figures
Personal demographic variables (n = 240)
| Demographics | f | (%) |
|---|---|---|
| Gender | ||
| Female | 96 | 40 |
| Male | 144 | 60 |
| Age | ||
| 18–26 | 51 | 21.25 |
| 27–35 | 42 | 17.50 |
| 36–44 | 85 | 35.42 |
| 45–53 | 43 | 17.92 |
| 54 and over | 19 | 7.91 |
| Marital status | ||
| Single | 51 | 21.25 |
| Married | 189 | 78.75 |
| Education level | ||
| Primary school graduate | 142 | 59.17 |
| High school graduate | 37 | 15.42 |
| Bachelor’s degree | 3 | 1.25 |
| Associate’s degree | 58 | 24.16 |
Model fit coefficients
| Fit index | Initial model values | Modified model values | One factor model values | Acceptable model fit levels | References |
|---|---|---|---|---|---|
| χ2 | 2,493.469; p = 0.000 |
759.609; p = 0.000 |
3,385.726; p = 0.000; Δ χ2 = 2,626.12 |
Low χ2 value; p < 0.01; p > 0.05 |
Hooper et al. (2008) |
| χ2/df | 6.203 | 1.928 | 8.360 | χ2/df < 3 χ2/df < 2 |
Wheaton et al. (1977), Tabachnick and Fidell (2007) |
| RMSEA | 0.149 | 0.063 | 0.177 | RMSEA < 0.05 – Good RMSEA < 0.08 – Acceptable |
Steiger (2007), Hu and Bentler (1999) |
| SRMR | 0.565 | 0.036 | 0.605 | 0.00≤SRMR ≤ 0.05 Good 0.05≤SRMR ≤ 0.10 Acceptable SRMR ≤ 0.08 |
Hu and Bentler (1999), Byrne (1998) |
| GFI | 0.475 | 0.826 | 0.414 | 0.95 ≤ GFI ≤ 1 Good 0.90 ≤ GFI ≤ 0.95 Acceptable |
Miles and Shevlin (2007), Tabachnick and Fidell (2007) |
| AGFI | 0.392 | 0.795 | 0.327 | 0.90 ≤ AGFI ≤ 1.00 Good 0.85 ≤AGFI ≤ 0.90 Acceptable |
Tabachnick and Fidell (2007) |
| CFI | 0.565 | 0.924 | 0.380 | 0.95 ≤ CFI ≤ 1.00 Good 0.90 ≤ CFI ≤ 0.95 Acceptable |
Schumacker and Lomax (1996), Hu and Bentler (1999) |
| IFI | 0.568 | 0.925 | 0.384 | 95 ≤ IFI ≤ 1.00 Good 0.90 ≤ IFI ≤ 0.95 Acceptable |
Miles and Shevlin (2007) |
| NNFI (TLI) | 0.529 | 0.916 | 0.334 | 0.97 ≤ NNFI ≤ 1 Good NNFI > 0.90 Acceptable |
Hu and Bentler (1999), Fan et al. (1999), Bentler and Bonett (1980) |
χ2 discrepancy chi square; χ2/df (chi square/degrees of freedom); CFI (comparative fit index); IFI (incremental fit index); RMSEA (root mean square of error approximation); NNFI (non-normed fit index) ; SRMR (standardized root mean square residual); GFI (goodness of fit index); AGFI (adjusted goodness of fit); and TLI (Tucker–Lewis index)
Discriminant and convergent validity of the model
| α | CR | AVE | MSV | MaxR(H) | Boreout | MOBBING | SIP | |
|---|---|---|---|---|---|---|---|---|
| Boreout | 0.837 | 0.701 | 0.512 | 0.300 | 0.961 | 0.715a | – | – |
| Mobbing | 0.854 | 0.854 | 0.541 | 0.300 | 0.863 | 0.548* | 0.736a | – |
| SIP | 0.920 | 0.767 | 0.539 | 0.276 | 0.868 | −0.446* | −0.525* | 0.734a |
α = Cronbach’s alpha; MaxR(H) = maximum reliability; asquare root of AVE value; CR = Composite reliability; AVE is the significant over the 0.50 level, the acceptable value of CR is 0.7 and above; AVE = average variance extracted; MSV = maximum shared variance; *Pearson correlation
Mediation analysis results
| Boreout | Total effects | Direct effects | Indirect effects | Lower bounds | Upper bounds | p | Mediation effect |
|---|---|---|---|---|---|---|---|
| H9. Mobbing-SIP | 0.018 | 0.016 | 0.002 | −0.141 | −0.021 | 0.003** | Partially (TVE:27.6%) |
The confidence interval values for indirect effects were calculated by bootstrap with N = 240. TVE: total variance explained; ***p is significant at 0.01 (two-tailed); *significant at 0.1 (two-tailed); **p is significant at 0.05 (two-tailed); if zero (0) does not match within the confidence interval values, the indirect effect is significant
Research hypotheses test results for direct effects
| No | Alternative hypotheses | Conclusion | t(2400) | β | p |
|---|---|---|---|---|---|
| H1 | MOB → Boreout | Supported | 6.039 | 0.495 | 0.005 |
| H2 | MOB → SIP | Supported | −5.602 | −0.436 | 0.005 |
| H3 | Boreout → SIP | Supported | −2.407 | −0.139 | 0.01 |
| H4 | MOB → CMW | Not supported | 9.403 | 0.756 | 0.449 |
| H5 | MOB → JB | Supported | 3.621 | 0.449 | 0.005 |
| H6 | MOB → CG | Not supported | 1.196 | 0.209 | 0.232 |
| H7 | MOB → ESIB | Not supported | −0.020 | 0.630 | 0.984 |
| H8 | MOB → NSD | Not supported | 10.548 | 0.560 | 0.575 |
t: critical ratio; β: standard beta; *0.1 level (two-tailed); **p is significant at 0.05 level (two-tailed); ***p is significant at 0.01 level (two-tailed)
References
Agervold, M. and Mikkelsen, E. (2004), “Relationships between bullying, psychosocial work environment and individual stress reactions”, Work and Stress, Vol. 18 No. 4, pp. 336-351.
Basu, R. and Green, S.G. (1997), “Leader-member exchange and transformational leadership: an empirical examination of innovative behaviors in leader‐member dyads”, Journal of Applied Social Psychology, Vol. 27 No. 6, pp. 477-499.
Bentler, P.M. and Bonett, D.G. (1980), “Significance tests and goodness of lit in the analysis of covariance structures”, Psychological Bulletin, Vol. 88 No. 3, pp. 588-606.
Brown, T.A. (2015), Confirmatory Factor Analysis for Applied Research, Guilford Publications.
Byrne, B.M. (1998), “Structural equation modeling with lisrel”, Prelis and Simplis: Basic Concepts, Applications and Programming, Lawrence Erlbaum Associates, Mahwah, NJ.
Carmeli, A., Meitar, R. and Weisberg, J. (2006), “Self-leadership skills and innovative behavior at work”, International Journal of Manpower, Vol. 27 No. 1, pp. 75-90.
Duffy, M. and Sperry, L. (2011), Mobbing: Causes, Consequences, and Solutions, Oxford University Press.
Fan, X., Thompson, B. and Wang, L. (1999), “Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes”, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6 No. 1, pp. 56-83.
Field, A. (2005), Discovering Statistics Using SPSS, 2nd ed., SAGE, London.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Gefen, D., Straub, D. and Boudreau, M.-C. (2000), “Structural equation modeling and regression: guidelines for research practice”, Communications of the Association for Information Systems, Vol. 4 No. 1, pp. 2-77.
Hair, J.F., Jr, Hult, G.T.M., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications.
Harper, J. (2020), “Bullied by the best: why the bully paradigm is a bad fit for understanding the mob”, in Confronting Academic Mobbing in Higher Education: Personal Accounts and Administrative Action, IGI Global, pp. 29-43.
Hayes, A.F. (2013), Mediation, Moderation, and Conditional Process Analysis. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, Guilford Publications, New York, NY, pp. 1-20.
Hooper, D., Coughlan, J. and Mullen, M. (2008), “Structural equation modelling: guidelines for determining model fit”, Electronic Journal of Business Research Methods, Vol. 6 No. 1, pp. 53-60.
Hu, L. and Bentler, P.M. (1999), “Cutoff criteria for fit ındexes in covariance structure analysis: conventional criteria versus new alternatives”, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6 No. 1, pp. 1-55.
Hu, M.L.M., Horng, J.S. and Sun, Y.H.C. (2009), “Hospitality teams: knowledge sharing and service innovation performance”, Tourism Management, Vol. 30 No. 1, pp. 41-50.
Hussain, K., Konar, R. and Ali, F. (2016), “Measuring service innovation performance through team culture and knowledge sharing behaviour in hotel services: a PLS approach”, Procedia - Social and Behavioral Sciences, Vol. 224, pp. 35-43.
Hyatt, H.R., McEntarfer, E., McKinney, K.L., Tibbets, S. and Walton, D. (2014), “Job-to-Job (J2J) flows: new labor market statistics from linked employer-employee data”, US Census Bureau Center for Economic Studies Paper No. CES-WP-14-34.
Kline, R.B. (2016), Principles and Practices of Structural Equation Modeling, 4th ed., The Guilford Press, New York, NY.
Leymann, H. (1996), “The content and development of mobbing at work”, European Journal of Work and Organizational Psychology, Vol. 5 No. 2, pp. 165-184.
MacKenzie, S.B. and Podsakoff, P.M. (2012), “Common method bias in marketing: causes, mechanisms, and procedural remedies”, Journal of Retailing, Vol. 88 No. 4, pp. 542-555.
Marín, C. and Piñeros, O. (2019), “Mobbing in service companies in Colombia”, A Silent Enemy. In International Conference on Applied Human Factors and Ergonomics, Springer, Cham, pp. 415-423.
Miles, J. and Shevlin, M. (2007), “A time and a place for incremental fit indices”, Personality and Individual Differences, Vol. 42 No. 5, pp. 869-874.
Moeller, J., Ivcevic, Z., White, A.E., Menges, J.I. and Brackett, M.A. (2018), “Highly engaged but burned out: intra-individual profiles in the US workforce”, Career Development International, Vol. 23 No. 1, pp. 86-105.
Nunnally, J.C. (1976), Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY.
Ouedraogo, A. and Koffi, V. (2018), “Managing creativity and innovation in the cultural industries: evidence from three cultural organizations in Canada”, Management Review: An International Journal, Vol. 13 No. 2, pp. 34-60.
Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports in organizational research: problems and prospects”, Journal of Management, Vol. 12 No. 4, pp. 531-544.
Santamaría, L., Nieto, M.J. and Miles, I. (2012), “Service innovation in manufacturing firms: evidence from Spain”, Technovation, Vol. 32 No. 2, pp. 144-155.
Schmit, M.J. and Allscheid, S.P. (1995), “Employee attitudes and customer satisfaction: making theoretical and empirical connections”, Personnel Psychology, Vol. 48 No. 3, pp. 521-536.
Schumacker, R.E. and Lomax, R.G. (1996), A Beginner’s Guide to Structural Equation Modeling, Lawrence Erlbaum Associates, Publishers, Mahwah. NJ.
Steffgen, G., Sischka, P., Schmidt, A.F., Kohl, D. and Happ, C. (2016), “The Luxembourg workplace mobbing scale: psychometric properties of a short instrument in three different languages”, European Journal of Psychological Assessment, Vol. 35 No. 2, pp. 164-171.
Steiger, J.H. (2007), “Understanding the limitations of global fit assessment in structural equation modeling”, Personality and Individual Differences, Vol. 42 No. 5, pp. 893-898.
Stevens, J.P. (1992), Applied Multivariate Statistics for the Social Sciences, 2nd ed., Erlbaum, Hillsdale, NJ.
Stock, R.M. (2015), “Is boreout a threat to frontline employees' innovative work behavior?”, Journal of Product Innovation Management, Vol. 32 No. 4, pp. 574-592, doi: 10.1111/jpim.12239.
Stock, R.M. (2016), “Understanding the relationship between frontline employee boreout and customer orientation”, Journal of Business Research, Vol. 69 No. 10, pp. 4259-4268.
Tabachnick, B.G. and Fidell, L.S. (2007), Using Multivariate Statistics, 5th ed., Allyn and Bacon, New York, NY.
Vega, G. and Comer, D. (2005), “Sticks and stones may break your bones but words can break your spirit: bullying in the workplace”, Journal of Business Ethics, Vol. 58 Nos 1/3, pp. 101-109.
Wheaton, B., Muthen, B., Alwin, D.F. and Summers, G.F. (1977), “Assessing reliability and stability in panel models”, in Heise D.R. (Ed.), Sociological Methodology Jossey-Bass, San Francisco, pp. 84-136.
Williams, L.J., Hartman, N. and Cavazotte, F. (2010), “Method variance and marker variables: a review and comprehensive CFA marker technique”, Organizational Research Methods, Vol. 13 No. 3, pp. 477-514.
Woodruff, R.B. (1997), “Customer value: the next source for competitive advantage”, Journal of the Academy of Marketing Science, Vol. 25 No. 2, pp. 139-153.
Acknowledgements
It is recommended to research by adding organizational behavior variables such as leadership types, psychological capital, psychological empowerment and job satisfaction to the model of this study.