J Gambl Stud DOI 10.1007/s10899-014-9484-z ORIGINAL PAPER

Problem Gambling: One for the Money…? M. Flack • M. Morris

 Springer Science+Business Media New York 2014

Abstract Recent research indicates a diverse range of motivations may help explain problem gambling. However, the role of specific motivations in gambling behaviour is not well understood. The primary objective of the current study was to investigate the role of gambling motivations by comparing two competing models. Namely, the efficacy of monetary motivation model was compared to a model where the emotion focused motivations of excitement, escape, and ego were constrained as the only predictors of problem gambling scores. A sample of 2,033 respondents were drawn from the general community and completed a questionnaire concerning their gambling behaviours and beliefs about gambling as an escape, a social occasion, a way to win money, an exciting activity, and as a means to enhance self-importance. Comparison of the competing models revealed that gambling for the chance to win money was not the most prominent motivation in the prediction of problem gambling scores. Instead, the model that allowed the emotion focussed motivation to predict gambling problems was shown to provide a superior fit to the data. These findings underscore the importance of considering a range of motivational influences on gambling behaviour. Moreover, it appears the emotional aspects associated with gambling play a prominent role in sustained gambling behaviour. Keywords Problem gambling
Gambling problems
Motivations
Outcome expectancies
Structural equation modelling

Introduction One approach to increase the understanding of problem gambling behaviour is to employ multidimensional motivation-type models (Binde 2013; Lee 2013). Moreover, determining

M. Flack (&)
M. Morris School of Psychological and Clinical Sciences, Charles Darwin University, Darwin, NT 0909, Australia e-mail: [email protected]

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how different motivations interrelate, and their association with gambling problems, may help clarify the way motivations underpin continued gambling in the face of successive loses (Lee et al. 2007). This, of course, has implications for how educational and other gambling harm minimisation strategies may be informed. For instance, if gambling behaviour is principally sustained by an overly optimistic view that one can profit from the activity, then attempting to moderate these beliefs is of upmost importance (Blaszczynski et al. 2004; Ladouceur and Walker 1996; Wulfert et al. 2005). However, if other types of motivations are equally or more influential than perceptions of winning, they should also be considered. The primary purpose of this research was to examine whether gambling problems are preferentially explained by perceptions of winning or whether non-monetary motivations are the dominant predictors. There is a growing body of research which shows people gamble for a diverse range of non-monetary reasons. For example, casino patrons report gambling for fun and entertainment, seeking a rush, to be around others and enhance one’s self-image (Cotte 1997; Loroz 2004). Electronic gambling machine players report gambling as a form of socialisation and escape (Lalander 2006; Thomas et al. 2009) and horse race bettors view gambling as a leisure activity which is exciting, highly sociable, and an escape from routine (Neal 2005). Recreational or non-problem gamblers reasons for gambling include gambling for the rush and excitement, to be with friends and socialise, and to alleviate boredom or negative moods (McGrath et al. 2010; Neighbors et al. 2002) whereas problem gamblers express that gambling is a way to regulate emotional states (Ricketts and Macaskill 2003; Wood and Griffiths 2007). Notably, there is some commonality across the forms of gambling suggesting the reasons cited are not necessarily mutually exclusive to each activity or level of involvement. In fact, the essence of many of the cited reasons has been represented in general gambling motivation type models and, in turn, shown to relate to gambling involvement and gambling frequency (Chantal et al. 1995; Dechant and Ellery 2011; Fang and Mowen 2009; Lee et al. 2006). Also, there is empirical support for multi-dimensional motivation models in explaining gambling problems. For example, Rockloff and Dyer (2006) found that their 4Es model, which reflected the need to gamble for excitement (action seeking), escape (avoiding unpleasant emotions and social situations), esteem (avoiding negative self-appraisal), and excess (difficulty controlling impulses), predicted gambling problems. Similarly, Stewart and Zack (2008) found problem gambling scores were positively associated with the motivation constructs of enhancement (excitement), cope (escape), and socialisation; with enhancement and cope emerging as independent predictors. In contrast to multi-dimensional motivation approaches, cognitive perspectives posit that problem gambling is primarily about the expectation of financial gain (Ladouceur and Walker 1996). Support for this premise is reflected in research which shows that cognitive biases are positively related to gambling problems (e.g., MacKillop et al. 2006; Mattson et al. 2008; Myrseth et al. 2010). Similarly, gambling simulation studies show that gambling intensity and excitement are contingent on the perception of pecuniary gain (Ladouceur et al. 2003; Moodie and Finnigan 2005; Wulfert et al. 2005). In particular, Wulfert et al. (2008) found that there was a concomitant change in gambling intensity as the monetary value of the stake increased. Also, supporting the proposition that monetary attraction of gambling facilitates gambling, Wohl et al. (2014) found that increasing students’ perceived need for money led to more positive views of gambling as having financial utility, which, in turn, led to more gambling. Although this research underscores the potential of perceived financial gain (or the perceived ability to influence the

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outcomes) as instrumental in gambling behaviour, it does not preclude the notion that nonfinancial gambling motivations are involved in the initiation and maintenance of problem gambling. Several studies have examined the relationship between gambling problems and monetary and non-monetary motivations, although the findings are somewhat mixed. For instance, there is evidence that problem gamblers report more positive views towards gambling as a way to win money, socialise, and increase positive mood than non-problem gamblers (Clarke and Clarkson 2007; Lee et al. 2007; Lloyd et al. 2010; Oei and Raylu 2010). Yet other studies have revealed only the non-monetary motivations differentiate problem and non-problem gamblers (Clarke 2004; Clarke et al. 2007; Ladouceur et al. 1997; Walters and Contri 1998). Further, some research has shown the salience of the expectation of winning money diminishes when other motivations are taken into account. For example, Clarke and Clarkson (2007) study of adults over the age of 65 found the relationship between financial motivation and gambling problems was not significant when the shared variance between emotion and social types of motivation and gambling problem scores were controlled. Oei and Raylu (2010) reported similar findings with adults recruited from the general community. One approach to more closely scrutinise the role of monetary and non-monetary motivations is to model their effects on gambling problems using competing models. Specifically, this involves constraining the beliefs about the non-financial utility of gambling to realise their influence via increased (or decreased) optimism about the chances of winning, whereas as an alternative model constrains non-monetary motivations to relate to problem gambling. Lee et al. (2007) used such an approach with participants recruited from a problem gambling treatment centre and compared the appropriateness of a money mediation model with an alternative model. The findings revealed the monetary mediation model was a superior fit to the non-monetary mediation model. Namely, the effects of nonmonetary motivations directed their influence via monetary motivation rather than directly predicting problem gambling scores. Excitement and avoidance types of motivation independently predicted monetary motivation, which in turn, predicted problem gambling severity. Of interest though, is the absence of research which directly compares competing motivation models in predicting problem gambling severity in the general community. This is somewhat of a concern given the need to acquire a better understanding of gambling problems as they occur across a continuum (Korn et al. 2003; Peller et al. 2008; Williams et al. 2007). Purpose of the Present Study The primary objective of the current study was to investigate the role of gambling motivations by comparing two competing models. Specifically, the efficacy of monetary motivation model was compared to a model where the emotion focused motivations were constrained as the only predictors of problem gambling scores. However, before the influences of different types of motivation can be compared, the characteristics of the motivation dimensions need to be identified. Indeed, from previous research it is clear several types of motivation should be represented. Two of the commonly cited types of emotion oriented motivations are gambling to increase positive mood (e.g., gambling for the stimulation and excitement) and gambling to distract from aversive mood states (escape). Gambling for stimulation is considered a form of positive reinforcement whereas escape is negative reinforcement (Stewart and Zack 2008). A third emotion focussed motivation is gambling to improve a sense of ego. For instance, it is

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suggested that gambling to enhance a sense of self-importance and self-worth may be central to gambling more than one can afford (Jacobs 1986; Rockloff and Dyer 2006; Walters and Contri 1998). Another non-monetary dimension captured in much of the recent research is gambling for socialisation. Although gambling for socialisation does not necessarily reflect a desire to engage with others, it may facilitate a desire to connect and interact with others (Thomas et al. 2013). Finally, central to the money mediation model, are the perceptions of the chance to win money at gambling (Lee et al. 2007). Three propositions were tested in the current study. First, it was expected that gambling motivations, as represented by excitement, escape, ego, social, and money gambling outcome expectancies, would emerge as five distinct constructs. Second, on the basis of previous research, it was anticipated that the money mediation model would be too restrictive and not represent the nature of the relationships between the variables of interest. Specifically, it was hypothesised that the escape, excitement and the ego facets of gambling expectancies would have a direct relationship with gambling problems. The final objective was to explore the role of the same aspects of gambling motivation in relation to gambling frequency. However, there were no prior assumptions about the prominence of specific types of motivation in explaining the frequency of gambling. Thus, paths between the five facets of expectancies and gambling frequency were specified, and the parameter estimates were allowed to be freely estimated. Figures 1 and 2 show the proposed theoretical models to be compared.

Methods The current study is part of a longitudinal study of gambling behaviour. Therefore, only the sample characteristics, measures, and procedure relevant to the first wave of the study are reported here. Participants A sample of 2,121 participants was drawn from a community-based population. Of these, 428 volunteers (246 females, 162 males and 20 unspecified) were part of the pilot study. These respondents were recruited via advertisements placed in newspapers, on a community service organisation web site, on two first year psychology unit web sites, and through networks of the researcher. The mean age of the volunteers was 38.33 (SD = 12.17). The majority of the respondents identified as Caucasian (72.4 %). Seven per cent of the respondents indicated they were Indigenous, 6.3 % Asian, 3.7 % as other, and 10.6 % did not report their ethnicity. No inducements were offered to any respondents. A further 1,693 participants (869 female, 715 males and 109 unspecified) were recruited using an online research panel. The mean age was 44.56 (SD = 16.30) and, similar to the pilot study, the majority of the sample reported they were Caucasian (71.4 %). Less than one per cent (0.7 %) identified as Indigenous, 8.2 % as Asian, 3.5 % as other, and 16.2 % did not report their ethnicity. The research panel members who completed the questionnaire were awarded a nominal incentive for their participation. There was no association between problem gambler status, as assessed by a PGSI score of 8 or more, and sample membership, v2 (5, n = 1,991) = 0.02, p = .90. The overall rate of problem gambling rate was 6.8 %.

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Fig. 1 Money mediation model

Fig. 2 Non-monetary model

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Procedure The main pool of respondents was acquired via a commercial research company. The research company was employed to send an email invitation with the questionnaire’s URL to a sample drawn from their membership base of approximately 400,000 Australians. The sampling frame parameters were set to reflect an Australian national representative sample in terms of age, gender, and geographical location. State and Territory jurisdictions were sampled according to national population distribution. From the 7,499 invitations sent, 1,693 members responded (22.7 % response rate). Those who completed the questionnaire were credited with 100 membership reward points (approximately AU$1.00 in value) and received ten competition entries. Ethics approval was obtained from Charles Darwin University Human Ethics Committee to conduct the current study. The only exclusion criterion was that participants were required to be at least 18 years of age. Measures Gambling Outcome Expectancies Scale (GOES) The GOES was developed to measure the reasons or types of motivation for gambling that are relevant to the spectrum of gambling behaviour that occurs across the general community. It was decided to assess gambling motivations with belief-based statements, or the expectancies about the outcomes of gambling, because such measures do not refer to how often gambling occurs. Rather, the beliefs about the perceived outcomes gambling, independent of gambling frequency, are assessed. Support for this approach has been forthcoming in previous research (e.g., Gillespie et al. 2007, Walters and Contri 1998), which has demonstrated the ability of gambling expectancies to delineate between distinct, but related, constructs. Given the absence of a belief-based measure to reflect the dimension of excitement, escape, ego, social, and money expectancies, the GOES was developed for the current study. A pool of 25 items was compiled by drawing on the existing scales cited in previous research (e.g., Chantal et al. 1995; Gillespie et al. 2007; Lee et al. 2007, Walters and Contri 1998). Five items were composed to link gambling to each of the identified attributes. For example, the item ‘gambling is about enjoying intensive feelings’ was used to represent the construct of excitement, whereas ‘gambling is the best way to relax’ was used as an indicator of escape. Each of the 25 items was rated on a 6-point Likert scale from strongly disagree (1) to strongly agree (6). The psychometric properties of the scale are reported in the results. Gambling Problems The Gambling Problem Severity Index (PGSI) from the Canadian Problem Gambling Index ([CPGI] Ferris and Wynne 2001) was employed to assess gambling problems. The 9-item scale assesses problem gambling behaviours (e.g., How often have you borrowed money or sold anything to get money to gamble? How often has your gambling caused any financial problems for you or your household?) The items are scored on 5-point Likert scales ranging from (0) never to (4) almost always. Cut off points can be used to indicate no risk (0), low risk (1–2), moderate risk (3–7), and problem gambling (8 or more). Validation studies suggest the PGSI forms a unitary factor structure and support the appropriateness of the scale as a continuous measure of gambling problems in non-clinical settings (Brooker et al. 2009; Holtgraves 2009; Sharp et al. 2011). The internal consistency of the PGSI in the current study was good (a = .93).

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Gambling Frequency Scale The Gambling Frequency Scale (GFS; Moore and Ohtsuka 1999) was used to assess pastyear gambling participation. The GFS consists of nine items, each of which refers to a different form of gambling (e.g., played cards for money, bet on sports, and played poker machines at the casino). The items were rated on a 5-point scale from (1) never or rarely, (2) once a year, (3) less than once a month, (4) monthly or more, (5) weekly or more. Responses were summed to provide a total gambling frequency score ranging from 9 to 45. High scores reflect a higher frequency of gambling. Moore and Ohtsuka reported an internal consistency of .71. The Cronbach alpha for the current study was .72. Demographics The respondents were asked to report their age, gender, employment status, income bracket, ethnicity, and education level.

Results Analytical Approach and Data Preparation The analysis was progressed in two main sections. First, the adequacy of the belief-based measures was tested with exploratory factor analysis and subsequently with confirmatory factor analysis. Second, the fit of the two proposed models was examined using covariance structural equation modelling and models’ fit compared. Prior to performing the analyses, the data were screened for aberrant responses. Ninety seven cases (7 from volunteer sample and 90 from the main pool) were removed from the analyses because of incomplete data ([10 % missing) or peculiar responses (e.g., reported not to gamble but endorsed the PGSI). Exploratory Factor Analysis The data collected from the sample of volunteers (i.e., those who participated in the pilot study) were used in the exploratory factor analysis. Preliminary inspection of the data indicated the items were suitably correlated to warrant data reduction with Kaiser–Meyer– Olkin measure of sample adequacy statistic was above .6 (.93) and Bartlett’s test of sphericity was significant (v2 = 7,132.22, p \ .01). Principal Axis Factoring (PAF) was selected to extract the factors as it is the preferred option when examining the factorability of scales designed to reflect specific constructs (Tabachnick and Fidell 2001). Factors with an Eigen value of at least one were retained and an oblique (oblimin) rotation was used to help interpret the factor solution. Table 1 shows the factor loadings for the rotated pattern matrix. The PAF revealed a five factor solution, which explained 70.27 % of the total item variance. The factors accounted for 42.14, 9.44, 7.60, 6.61, and 4.49 % of the explained variance, respectively. Inspection of the rotated pattern matrix revealed each of the items loaded significantly (equal to or greater than .4) on the factors they were designed to reflect. Furthermore, the items loaded primarily on their respective factors apart from one item (Gambling gives a feeling of being really alive), which had a high cross loading on factor one. Each of the factors represented one of the proposed facets of the attraction to

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J Gambl Stud Table 1 Rotated pattern matrix of the GOES Item

Description

Factor 1

2

3

4

5

17

Gambling is about feeling like an expert

.77

.03

.06

.02

.01

18

Gambling produces a feeling of importance

.88

.01

.07

.00

-.02

19

Gambling is about feeling in control

.73

.06

-.09

.11

.08

20

Gambling is cool

.48

.23

-.06

.18

.06

21

Gambling produces a feeling of being powerful

.79

.06

.01

.10

.01

23

Gambling is a social occasion

-.02

.64

.26

.04

.02

24

Provides an opportunity to be with similar people

25

Gambling is a way to meet new people

26

Provides an opportunity to get along with others favourably

27

Gambling provides an opportunity to be with friends

1

It is exciting to play for money

-.06

.21

.52

.18

.04

2

Gambling is a rush

.03

.03

.66

.11

.10 -.03

.11

.75

.10

-.14

.08

-.01

.88

-.12

.06

.07

.11

.75

-.10

-.14

.08

-.05

.89

.03

.07

-.05

4

Winning feels great

-.01

.01

.59

-.01

5

Gambling is about enjoying intensive feelings

.29

.06

.56

.02

.11

6

Gambling gives a feeling of being really alive

.39

-.04

.41

.06

.24

3

Gambling is a way to win big money immediately

-.03

.02

.21

.67

-.01

7

Gambling provides a good chance to win big with small money

-.05

.02

.14

.68

.08 .28

13

Gambling is a way to solve financial problems

.16

-.14

-.07

.50

16

It is easy to make money at gambling

.20

.06

-.12

.66

.01

22

Gambling is a way to make big money

.14

.17

-.05

.71

-.06

8

Gambling is a way to forget everyday problems

.15

.01

.25

-.05

.53

9

Gambling is the best way to relax

-.05

.02

-.02

.14

.77

10

Gambling can help clear your mind

-.07

.04

.07

.02

.88

11

Gambling helps release tension

.01

.04

-.10

-.01

.82

12

Gambling provides an escape from responsibilities

.27

.08

.08

-.10

.42

Loadings above .4 are shown in boldface

gambling and can be considered to reflect, in numerical order, the constructs of Ego, Social, Excitement, Money, and Escape. The inter-correlation between the factors ranged between r = .27 and .54. These results provided initial support for the appropriateness of the five factor designation of gambling outcome expectancies as well as the design and development of the GOES instrument. Confirmatory Factor Analyses The factor structure of the GOES was further scrutinised with Confirmatory Factor Analysis (CFA). AMOS version 18 was employed to perform the analyses and the Maximum Likelihood method was used to estimate the model parameters. As the Chi square statistic is sensitive to large sample sizes and minor departure from normality (Kline 2005), several fit indices were employed to assess the fit of the model to data. The Standardised Root Mean Residual (SRMR) and the Root Mean Square Error of Approximation (RMSEA)

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were employed to provide a metric of absolute fit (the proportion of discrepancy between the proposed model and sample data) and the Comparative Fit Index (CFI) was used to assess the incremental fit (the degree of improvement of the specified model over a baseline model). In addition, the incremental fit index, the Tucker-Lewis Index (TLI) was employed as it takes into account model complexity (Hu and Bentler 1999). A SRMR estimate of less than .06 and RMSEA estimate of less than .08 are recognised as indicating a relatively close fit whereas an RMSEA estimate of more than .10 indicates a poor fit between the hypothesised model and observed data (Kline 2005). CFI and TLI values above .95 indicate the specified model is a good fit to the data (Hu and Bentler 1999). A two-step approach was adopted to test the measurement model using the approach suggested by Jo¨reskog (1993). That is, each of the five factors was examined separately (as one factor congeneric models) and respecified as required and then the fit of the full measurement model (the 5-factor GOES) was examined. The models were calibrated by trimming items which did not fit the measurement model. Removal of items was guided by the modification indices statistics and inspection of the residual matrix (e.g., standardised estimates with values [2.58) to detect areas of misspecification (Byrne 2010). Ultimately though, decisions were made on theoretical grounds and the model fit. To provide independent samples to test the measurement model, the sample obtained by the market research company (n = 1,603) was randomly divided into two groups. Sample A (n = 801) was employed to calibrate the model and sample B (n = 802) was used as the hold-out sample to cross validate the model. In total, seven items were removed to ensure the uni-dimensionality of the factors. One item was omitted from the escape, enhance, and social factors, and two items excitement and money factors. More specifically, the item ‘…escape from responsibilities’ was trimmed as it shared error variance other escape items, suggesting the item was perceived differently to the remaining items on this factor. The other items removed from the enhance, social, excitement and money factors were ‘gambling is cool’, ‘gambling is a social occasion’, ‘winning feels great’, ‘it is exciting to play for money’, ‘it is easy way to make a profit’, and ‘is a way to solve financial problems’, respectively. After re-specification, the five factors were combined in one measurement model with the factors allowed to co-vary. To ensure the uniqueness of the factor, items were permitted to load on only their respective factor. The fit estimates for the five factor GOES model indicated the model was a good fit to data the (SRMR = .04; RMSEA = .06; CFI = .97; TLI = .96). To test the robustness of the model, the fit of the five factor GOES model was assessed with the hold out sample (sample B). The model fit estimates were SRMR = .04; RMSEA = .06; CFI = .97; TLI = .96. The similarities between the fit estimates across sample A and B indicated the model was stable. Finally, the five factor model was compared to a more parsimonious model (a one-factor undifferentiated model). The full sample (n = 2,024) was employed, with the analyses yielding superior fit estimates for the five factor GOES model (SRMR = .04; RMSEA = .06; CFI = .96; TLI = .96) compared to the one factor undifferentiated model (SRMR = .11; RMSEA = .20; CFI = .61; TLI = .55). Taken together, these results support the appropriateness of a five factor GOES measurement model. Figure 3 shows the factor loadings and inter-correlation for the five factor GOES model with the full sample. On the basis of these findings, five summated subscales were created to reflect each of the dimensions of expectancies identified. The reliability coefficients (Cronbach’s alpha) for excitement, escape, ego, social and money were .85, .88, .84, .92 and .92, respectively. The subscales were used in the ensuing path analyses to test the structural relationship

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Fig. 3 CFA: Five-factor GOES with standardised parameter estimates

between the gambling outcome expectancies and gambling problems and gambling frequency. The correlation between the variables of interest is shown in Table 2. As shown in Table 2, the five dimensions of gambling outcome expectancies were correlated with gambling frequency, indicating the more respondents endorsed gambling as exciting, an escape, ego enhancing, as social and a way to win money, the higher the level of gambling involvement. Likewise, the same five aspects of gambling expectancies were associated with the number of gambling problems experienced, as assessed by the PGSI.

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J Gambl Stud Table 2 Correlations between GOES and indices of gambling behaviour Scale/subscale

1

2

3

4

5

6

7

1. Excitement

–

.60*

.53*

.51*

.51*

.28*

.33*

2. Escape

.60*

–

.53*

.49*

.44*

.35*

.38*

3. Enhance

.54*

.54*

–

.51*

.48*

.23*

.26*

4. Social

.52*

.49*

.52*

–

.39*

.30*

.19*

5. Money

.52*

.45*

.49*

.40*

–

.28*

.25*

6. Frequencya

.29*

.36*

.24*

.31*

.29*

–

.51*

7. Gambling problemsa

.33*

.38*

.28*

.21*

.27*

.52*

–

8. Age

-.04

.05

-.03

-.04

-.05

-.01

-.05

9. Gender

.12*

.10*

.18*

.11*

.14*

.11*

.13*

* Indicates significant at p \ .001 a

Indicates variable subjected to log linear transformation. Values above diagonal controlling for age and gender, below diagonal zero-order correlations

The correlations matrix indicated that males gambled more often, experienced more problems and held higher gambling expectancies than their female counterparts. A series of t-tests confirmed this observation, revealing males scored significantly higher on gambling frequency (t = 5.02, p \ .01), PGSI scores (t = 5.95, p \ .01) and the gambling outcome expectancies of excitement (t = 5.57, p \ .01), escape (t = 4.50, p \ .01), enhance (t = 7.95, p \ .01), social (t = 4.85, p \ .01), and money (t = 6.26, p \ .01). Given the gender differences, second order correlations were conducted to assess the impact of gender on the relationship between gambling expectancies and gambling behaviour. As shown in Table 2, there were no significant differences between the zero order and second order correlations for gambling behaviour and gambling problems and the gambling outcome expectancies (correlation coefficients all within .02). Taken together, this demonstrates that the association between the indices of gambling behaviour and expectancies was not due to the effects of gender. Consequently, gender was not controlled in subsequent analyses. Comparison of Models The models shown in Figs. 1 and 2 were compared using structural covariance modelling in AMOS version 18. Similar to the approach employed in the CFA, the CFI, TLI, RMSEA and SMRM indices were used to evaluate the fit of the proposed models to the data. However, as the two models were not nested (one is not a subset of the other), the AIC, CAIC and BIC were used to compare the models. Models with lower AIC, CAIC and BIC are considered to fit the data better than models with higher estimates (Kline 2005). Compared to the AIC, the CAIC takes into account sample size, and the BIC imposes a greater penalty for a reduction in degrees of freedom when calculating fit estimates (Byrne 2010). Prior to the performing the analysis, the PGSI and gambling frequency scores were transformed to reduce skewness and those who gambled once a year or less (n = 414) were excluded to improve the homoscedasticity of residuals. Figures 4 and 5 show standardised estimates for the specified paths and Table 3 displays the fit statistics for the respective models. The co-variances between the predictor variables (and the residuals terms and co-variance) were omitted from the diagrams to improve clarity.

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The monetary mediation model revealed that 33 % of money expectancies was explained by the non-monetary expectancies, with Excitement and Ego making significant contributions. Money, in turn, explained 7 % of the variance in problem gambling. A total of 13 % of the variance was explained in gambling frequency. Money, Escape, and Social independently contributed to the prediction of gambling frequency. However, the monetary mediation model failed to reach an acceptable level of fit (see Table 3). The parallel model was consistent with expectations. Excitement, Escape and Enhancement all contributed to the prediction of 18 % of the variance in gambling problems. Whereas the expectancies dimensions of Escape, Money, and Social, together made a significant contribution to the prediction of 19 % of the variance in gambling frequency. The model displayed a good fit, with all fit indices converging at an acceptable level (see Table 3). Further, the non-monetary model fitted the data better than the monetary mediation model as shown by the comparative fit indices (AIC, CAIC, and BIC). Figure 6 shows the parallel model with the non-significant paths removed.

Discussion A growing body of research suggests that gambling may be influenced by a diverse range of reasons or motivations (Binde 2013). However, it is unclear whether certain types of motivations are more salient than others in explaining gambling behaviour. To this end, the current research investigated the role of gambling motivations by comparing two competing models. Specifically, the efficacy of monetary motivation, as the primary explanatory motivation, was compared to a model where the emotion focused motivations of excitement, escape, and ego were constrained as the only predictors of problem gambling scores. In addition, the roles of the same motivations were used to explain gambling frequency. Prior to comparing these different models, the ability of the gambling outcome expectancies to differentiate between the interrelated monetary and non-monetary facets of gambling motivation was assessed. The findings from a series of factor analyses supported the hypothesis that gambling motivations could be meaningfully considered as five discrete aspects of motivation. Initial support for the five factor structure of expectancies measures was found using an exploratory factor analysis (EFA) with the pilot survey data. Beliefs about gambling as a way to increase positive mood (excitement), moderate negative moods (escape), increase a sense of self-importance (ego), a way to be around other people (social), and a chance to win money (money) were found to form separate, but related, dimensions. Confirmatory factor analysis was employed to further scrutinise the integrity of each of the factors with independent samples. Although some re-specification was required (i.e., removal of items) to increase the acuity of the subscale, each of the factors were determined as onedimensional, and the results mirrored those from the EFA. Of particular interest was the emotion oriented measure of gambling expectancies delineated into separated facets. Namely, gambling for escape and excitement emerged as different constructs, supporting the premise that these motivations may reflect different types of reinforcement (Stewart and Zack 2008). In relation to the comparison of the monetary mediation and non-monetary model, the expectation that the former model was too restrictive was supported. That is, although the money mediation model accounted for some variance in problem gambling, the fit statistics indicated the model was misspecified. In contrast, when the motivations of excitement, escape, and enhancement were freed to predict problem gambling scores, and the monetary

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Fig. 4 Money mediation model with standardised estimates. *Indicates significant at p \ .05; **indicates significant at p \ .01

Fig. 5 Non-monetary model with standardised estimates. *Indicates significant at p \ .05; **indicates significant at p \ .01

and social pathways constrained (i.e., the non-monetary model), the model fitted the data. Together, these findings suggest gambling for the chance to win more is not the most prominent motivation in problematic gambling. Instead, the emotion focussed motivation better accounted for the relationships apparent in the data.

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J Gambl Stud Table 3 Measure of fit indices for models money mediation and parallel models Model

v2

Mediation Non-monetary Non-monetary with ns paths removed a

df

pa

SRMR

RMSEA

CFI

TLI

AIC

CAIC

BIC

206.68

4

\.001

.084

.180

.943

.702

254.68

407.26

383.26

8.46

2

0.48

.011

.045

.998

.988

60.46

225.76

199.76

14.13

4

0.15

.014

.040

.997

.985

62.13

214.72

190.72

Bollen-stine bootstrapped estimate

Fig. 6 Non-monetary model with standardised estimates and non-significant paths removed. *Indicates significant at p \ .05; **indicates significant at p \ .01

The anticipation that excitement, escape, and ego expectancies would directly contribute to the explanation of problem gambling, and monetary expectancies would not, was consistent with expectations. Specifically, these results are comparable to Clarke and Clarkson (2007) and Oei and Raylu (2010) who found that monetary motivations did not uniquely contribute to the explanation of gambling problems when non-financial gambling motivations were taken into account. This may suggest, despite the chance to win money being a central characteristic of gambling, the optimism about winning money is not the main reason that some people transition to problem gambling. Rather, it appears gambling for the buzz, a way to feel dynamic, and to relieve stress play a greater role in protracted gambling in the face of mounting losses than optimism about winning. This could also imply, if problems occur as a result of gambling, then the increase in positive mood and the sense of relief that gambling affords becomes increasingly beneficial to the gambler. Of course, the desire or need to regulate emotional states by engaging in gambling activities may not necessarily stem from gambling itself. Rather, external stressors or pre-existing psychological aversive emotional states may be the catalyst for problematic gambling

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behaviour (Wohl et al. 2008). Nonetheless, the current findings support the contention gambling is, in part, sustained by both positive and negative forms of emotional reinforcement (Stewart and Zack 2008). Another interesting finding was the different associations (or absence of) between social expectancies and gambling problems and gambling frequency. In relation to gambling problems, the findings were congruent with Stewart and Zack (2008) and Thomas et al. (2009). These researchers found social motivation was less strongly associated with gambling problems than escape motivations. As Thomas et al. suggest, the social aspects of gambling may be relevant to gamblers regardless of gambling difficulties, meaning social motivations may not serve as a differentiating factor between those experiencing gambling problems and those who are not. The findings from the current study certainly support this contention. However, it is also interesting that the perceived socialness of gambling remained a significant predictor of gambling frequency when the influences of the other motivations were taken into account. Perhaps the social aspects gambling do not directly contribute to problem gambling, but nevertheless remain a significant feature of gambling. The social connectedness acquired through gambling, and the loss of social networks outside of gambling, may make reducing gambling behaviours difficult (Ocean and Smith 1993). From an applied perspective, this could indicate the social aspects of gambling should be considered as a factor in measures employed to moderate gambling behaviour. Also, it was interesting to note that the escape, social, and monetary expectancies were independently associated with gambling frequency, but not gambling problems. In other words, gambling frequency was related to a wider range of motivations than gambling problems. It is possible that gambling for a diverse set of reasons is a protective factor against problem gambling, whereas gambling predominately for the emotional regulation afforded is a risk factor. However, as mentioned before, an alternate explanation is the reasons for gambling may change over time. Thus, gambling may initially be motivated by the social attraction, opportunity to relax, and the hope of winning and, if gambling-related problems begin to accrue, then emotional regulation (excitement and escape) become of primary importance. Irrespective of the direction of the relationship between emotionfocussed motivations and gambling problems, educational initiatives that raise awareness of risks of gambling to moderate mood states may provide a way to reduce problematic gambling.

Conclusions, Limitations and Future Research The current study provides insights into the interrelationship between the reasons for gambling and gambling behaviour. Also, as gambling frequency and intensity of involvement is considered a risk factor of problem gambling (Currie et al. 2006; Rodgers et al. 2009), exploring the motivations that underpin gambling participation and, not just problem gambling, allowed this area to be further explored. However, as is the case with much of the previous research on gambling motivations, the observations in the current study can only be interpreted in light of the constraints of cross sectional research. More specifically, whether self-reported motivations play a causal role in sustained gambling behaviour or only reflect the self-justification of behaviour cannot be determined from this study. One approach to further probe the role of gambling motivations is to employ longitudinal studies to assess whether gambling motivations change in concert with gambling behaviour or if certain motivations foreshadow later gambling problems. Another approach is to examine explanatory utility of gambling motivations in the context

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of known problem gambling risk factors. For example, gambling motivations may be an intervening variable between aversive psychological states and gambling problems (Lloyd et al. 2010). Further research is needed to assess whether the pattern of relationships identified in the current study would hold in other samples. Namely, the present study’s sample was recruited using an online survey panel and, as such, may not fully reflect the gambling motivation profile of those who are likely to be at risk of gambling problems. Purposively targeting regular non-problem gamblers and problem gamblers would allow the generalisability of the current findings to be further explored. It is also important to note that the current study’s findings may not generalise to treatment seeking samples. For instance, it is possible that people who have gambled problematically for a substantial period of time may view chasing their losses as their only hope of recouping their finances and, consequently, moderating these beliefs is of primary importance (Lee et al. 2007; Ladouceur and Walker 1996). However, in terms of harm minimisation education initiatives, the current findings indicate a focus should be given to the non-monetary aspects of gambling to ensure consumers recognise the emotional allure of gambling. Taken together, findings from the current study help clarify the nature of the relationship between gambling motivations and behaviour. By employing a clearly defined and psychometrically sound measure of gambling motivations, the perception about the chance of winning money at gambling were shown not to be central to gambling problems. Paradoxically, this suggests a need for educational and other gambling harm minimisation strategies not to focus solely on beliefs about the profitability of gambling, but to take into account the non-monetary aspects of gamblers experience. It is suggested that by doing so the effectiveness and relevance of such measure, for those who chose to gamble, may be enhanced.

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