High Stakes: Performance and Engagement Outcomes of Gambling Interference with Work and Nonwork Anna J. Lorys, Kimberlye E. Dean, Laura N. Provolt, Melissa E. Mitchell, Cavan J. Gray, Lillian T. Eby University of Georgia High Stakes: Performance and Engagement Outcomes of Gambling Interference with Work and Nonwork Anna J. Lorys, Kimberlye E. Dean, Laura N. Provolt, Melissa E. Mitchell, Cavan J. Gray, Lillian T. Eby University of Georgia IntroductionResults Conclusions References Carlson, D. S., Kacmar, K. M., & Williams, L. J. (2000). Construction and initial validation of a multidimensional measure of work-family conflict. Journal of Vocational Behavior, 56, Dickson-Swift, V. A., James, E. L., & Kippen, S. (2005). The experience of living with a problem gambler: Spouses and partners speak out. Journal of Gambling Issues, 13. Ferland, F., Fournier, P. M., Ladouceur, R., Brochu, P., Bouchard, M., & Pâquet, L. (2008). Consequences of pathological gambling on the gambler and his spouse. Journal of Gambling Issues, 22, Frone, M. R., Yardley, J. K., & Markel, K. S. (1997). Developing and testing an integrative model of the work–family interface. Journal Of Vocational Behavior, 50, Griffiths, M. (2009). Internet gambling in the workplace. Journal of Workplace Learning, 21, McComb, J. L., Lee, B. K., & Sprenkle, D. H. (2009). Conceptualizing and treating problem gambling as a family issue. Journal of Marital and Family Therapy, 35, O'Boyle Jr, E. H., Forsyth, D. R., Banks, G. C., & McDaniel, M. A. (2012). A meta-analysis of the dark triad and work behavior: A social exchange perspective. Journal of Applied Psychology, 97, 557. Raylu, N., & Oei, T. P. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22, Rich, B., LePine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and effects on job performance. Academy Of Management Journal, 53, Carlson, D. S., Kacmar, K. M., & Williams, L. J. (2000). Construction and initial validation of a multidimensional measure of work-family conflict. Journal of Vocational Behavior, 56, Dickson-Swift, V. A., James, E. L., & Kippen, S. (2005). The experience of living with a problem gambler: Spouses and partners speak out. Journal of Gambling Issues, 13. Ferland, F., Fournier, P. M., Ladouceur, R., Brochu, P., Bouchard, M., & Pâquet, L. (2008). Consequences of pathological gambling on the gambler and his spouse. Journal of Gambling Issues, 22, Frone, M. R., Yardley, J. K., & Markel, K. S. (1997). Developing and testing an integrative model of the work–family interface. Journal Of Vocational Behavior, 50, Griffiths, M. (2009). Internet gambling in the workplace. Journal of Workplace Learning, 21, McComb, J. L., Lee, B. K., & Sprenkle, D. H. (2009). Conceptualizing and treating problem gambling as a family issue. Journal of Marital and Family Therapy, 35, O'Boyle Jr, E. H., Forsyth, D. R., Banks, G. C., & McDaniel, M. A. (2012). A meta-analysis of the dark triad and work behavior: A social exchange perspective. Journal of Applied Psychology, 97, 557. Raylu, N., & Oei, T. P. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22, Rich, B., LePine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and effects on job performance. Academy Of Management Journal, 53, Gambling is an increasingly prevalent and socially acceptable form of recreation (McComb, Lee, & Sprenkle, 2009). However, we know that frequent gambling can interfere with responsibilities in both work and nonwork domains. In particular, previous qualitative research finds that gambling can interfere with time spent on leisure activities and with family and friends, and can result in social isolation and problems at work (Dickson-Swift, James, & Kippen, 2005; Ferland et al., 2008). Other authors suggest that gambling can be costly to organizations through its effects on gamblers’ concentration and productivity (Griffeths, 2009). The current study quantitatively examines how gambling interferes with work and nonwork, and how this interference may relate to performance and engagement in each respective domain. More specifically, this study examines whether gambling interfering with work (GIW) and gambling interfering with nonwork (GINW) are associated with performance and engagement outcomes in work and nonwork domains, respectively. By looking at the effects of GIW and GINW over and above general work-nonwork conflict, we examine the unique effects of gambling on the work-nonwork interface. Gambling is an increasingly prevalent and socially acceptable form of recreation (McComb, Lee, & Sprenkle, 2009). However, we know that frequent gambling can interfere with responsibilities in both work and nonwork domains. In particular, previous qualitative research finds that gambling can interfere with time spent on leisure activities and with family and friends, and can result in social isolation and problems at work (Dickson-Swift, James, & Kippen, 2005; Ferland et al., 2008). Other authors suggest that gambling can be costly to organizations through its effects on gamblers’ concentration and productivity (Griffeths, 2009). The current study quantitatively examines how gambling interferes with work and nonwork, and how this interference may relate to performance and engagement in each respective domain. More specifically, this study examines whether gambling interfering with work (GIW) and gambling interfering with nonwork (GINW) are associated with performance and engagement outcomes in work and nonwork domains, respectively. By looking at the effects of GIW and GINW over and above general work-nonwork conflict, we examine the unique effects of gambling on the work-nonwork interface. Employed, frequent gamblers (i.e., gambled weekly or more; N = 115) were recruited through Amazon’s Mechanical Turk website, and were paid $1.10 for completing an online survey. The average age of participants was 32 years, and the majority identified as male (n = 74, 65%) and Caucasian (n = 89, 77%). Participants worked 40 hours per week on average and were employed in a variety of occupations. All study variables were measured using multi-item, reliable self-report measures. GIW, GINW, work interfering with family (WINW), and nonwork interfering with work (NWIW), were assessed with items adapted from Carlson et al.’s (2000) measure. Work and nonwork performance were measured using Frone et al.’s (1997) scale, and work and nonwork engagement were measured using the cognitive engagement subscale of Rich et al.’s (2010) job engagement measure. Employed, frequent gamblers (i.e., gambled weekly or more; N = 115) were recruited through Amazon’s Mechanical Turk website, and were paid $1.10 for completing an online survey. The average age of participants was 32 years, and the majority identified as male (n = 74, 65%) and Caucasian (n = 89, 77%). Participants worked 40 hours per week on average and were employed in a variety of occupations. All study variables were measured using multi-item, reliable self-report measures. GIW, GINW, work interfering with family (WINW), and nonwork interfering with work (NWIW), were assessed with items adapted from Carlson et al.’s (2000) measure. Work and nonwork performance were measured using Frone et al.’s (1997) scale, and work and nonwork engagement were measured using the cognitive engagement subscale of Rich et al.’s (2010) job engagement measure. Correlations are shown in Table 1. Hierarchical multiple regression was used to examine the associations among GIW, GINW, and performance and engagement in each respective domain (e.g., GIW as predictor of work performance and work engagement, GINW as predictor of nonwork performance and nonwork engagement). In order to examine whether GIW or GINW predicted variance in the dependent variables above and beyond work- nonwork conflict, we controlled for nonwork interfering with work in the regressions predicting work outcomes, and for work interfering with nonwork in the regressions predicting nonwork outcomes. GIW was negatively related to work engagement (ß = -.29, p <.05) and work performance (ß = -.57, p <.05) (see Table 2). By contrast, GINW was not to related to nonwork engagement (ß = -.07, n.s.) but it was negatively related to nonwork performance (ß = -.31, p <.05) (see Table 3). These results indicate that GIW and GINW predict performance in work and nonwork domains respectively, and that these effects persist above and beyond the effects of work-nonwork conflict. The effects for engagement were mixed. As this is the first study to examine GIW and GINW, additional research is needed to identify other outcomes of these types of interference. This might include satisfaction with work and nonwork, as well as effects on relationships in each respective domain (e.g., coworkers, supervisors, spouses, children). Research is also needed to identify predictors of GIW and GINW. It seems particularly important to examine specific aspects of gambling behavior (e.g., frequency and type of gambling, gambling losses) as predictors of GIW and GINW. Drawing from clinical research on gambling (Raylu & Oei, 2002) and the I-O literature on counterproductive behavior (O’Boyle et al., 2012), there may also be personality traits that increase the likelihood of gambling interference, such as narcissism and neuroticism. We hope that this study sparks additional I-O research on the effects of gambling on the work-nonwork interface. These results indicate that GIW and GINW predict performance in work and nonwork domains respectively, and that these effects persist above and beyond the effects of work-nonwork conflict. The effects for engagement were mixed. As this is the first study to examine GIW and GINW, additional research is needed to identify other outcomes of these types of interference. This might include satisfaction with work and nonwork, as well as effects on relationships in each respective domain (e.g., coworkers, supervisors, spouses, children). Research is also needed to identify predictors of GIW and GINW. It seems particularly important to examine specific aspects of gambling behavior (e.g., frequency and type of gambling, gambling losses) as predictors of GIW and GINW. Drawing from clinical research on gambling (Raylu & Oei, 2002) and the I-O literature on counterproductive behavior (O’Boyle et al., 2012), there may also be personality traits that increase the likelihood of gambling interference, such as narcissism and neuroticism. We hope that this study sparks additional I-O research on the effects of gambling on the work-nonwork interface. Method