J Gambl Stud DOI 10.1007/s10899-015-9557-7 ORIGINAL PAPER

Jackpot Structural Features: Rollover Effect and Goal-Gradient Effect in EGM Gambling En Li1 • Matthew J. Rockloff1 • Matthew Browne1 • Phillip Donaldson1

Ó Springer Science+Business Media New York 2015

Abstract Relatively little research has been undertaken on the influence of jackpot structural features on electronic gaming machine (EGM) gambling behavior. This study considered two common features of EGM jackpots: progressive (i.e., the jackpot incrementally growing in value as players make additional bets), and deterministic (i.e., a guaranteed jackpot after a fixed number of bets, which is determined in advance and at random). Their joint influences on player betting behavior and the moderating role of jackpot size were investigated in a crossed-design experiment. Using real money, players gambled on a computer simulated EGM with real jackpot prizes of either $500 (i.e., small jackpot) or $25,000 (i.e., large jackpot). The results revealed three important findings. Firstly, players placed the largest bets (20.3 % higher than the average) on large jackpot EGMs that were represented to be deterministic and non-progressive. This finding was supportive of a hypothesized ‘goal-gradient effect’, whereby players might have felt subjectively close to an inevitable payoff for a high-value prize. Secondly, large jackpots that were non-deterministic and progressive also promoted high bet sizes (17.8 % higher than the average), resembling the ‘rollover effect’ demonstrated in lottery betting, whereby players might imagine that their large bets could be later recouped through a big win. Lastly, neither the hypothesized goal-gradient effect nor the rollover effect was evident among players betting on small jackpot machines. These findings suggest that certain highvalue jackpot configurations may have intensifying effects on player behavior. Keywords Electronic gaming machine (EGM)
Jackpot
Progressive
Deterministic
Jackpot size
Gambling intensity

& En Li [email protected] 1

Central Queensland University, Queensland 4701, Australia

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Introduction With the number of electronic gaming machines (EGMs) exceeding 7.6 million worldwide (Ziolkowski 2014), an increasing amount of research has been conducted on various aspects of EGMs and EGM player behaviors (e.g., Blaszczynski et al. 2014; Browne et al. 2014; Harrigan et al. 2014; Millhouse and Delfabbro 2008; Rockloff et al. 2011, 2012, 2014; Sharpe et al. 2005; Wohl et al. 2013). Despite the existing research efforts, there is little direct evidence on the influence of the structural characteristics of EGM jackpots on gambling behavior (for a review, see Rockloff and Hing 2013). To redress this deficit, this present study examined two common structural features of EGM jackpots: progressive versus non-progressive jackpots, and deterministic versus non-deterministic jackpots. These features, as described below, were examined in an experimental design for their potential interactive effects on intensity of gambling, and for how jackpot size may moderate such effects.

Progressive Versus Non-progressive Jackpots: Rollover Effect or GoalGradient Effect? Progressive jackpots incrementally grow in value as players make additional bets. Alternatively, non-progressive jackpots generate a fixed dollar payout irrespective of the accumulation of bets and their contributions from the players. Two conflicting views can be postulated regarding whether progressive or non-progressive jackpots should have a more motivating effect on players’ gambling intensity. On the one hand, evidence from lottery betting (Beenstock and Haitovsky 2001; Farrell et al. 1999; Rogers 1998; Rogers and Webley 2001) suggests that progressive (vs. nonprogressive) jackpots may lead to a ‘rollover effect’ where gamblers are encouraged to bet more. For example, lottery sales figures in the UK tended to increase dramatically on weeks when rollover jackpots occurred (Rogers 1998; Rogers and Webley 2001). Furthermore, a time series analysis based on the British lottery sales found that jackpot rollovers boosted sales and potential addiction for the game (Farrell et al. 1999). The time series lottery data from Israel also confirmed a positive relationship between rollovers and ticket sales (Beenstock and Haitovsky 2001). If the rollover effect does apply to EGM jackpots, EGMs with progressive (vs. non-progressive) jackpots should lead players to bet more as each bet adds to the accumulated amount of the jackpots, and that amount may be seen as recoverable investment in the ultimate jackpot prize. An alternative point of view is that EGM players who consider hitting the jackpot as their goal may experience a ‘goal-gradient effect’ (Brown 1948; Hull 1934; Kivetz et al. 2006), where the efforts to fulfil a goal tend to be negatively related to the perceived distance to the goal. The goal-gradient effect was originally found in animal behaviors, such that animals ran faster and exerted stronger force when they drew closer to their food (Brown 1948; Hull 1934). Kivetz et al. (2006) proposed that the goal-gradient effect should also apply to humans, and demonstrated the motivating effect of shorter goal distance across a variety of human behaviors (e.g., coffee purchases; rating songs) and reward scenarios (e.g., buy ten coffees to earn one; rate 51 songs to earn US$25; Kivetz et al. 2006). According to the goal-gradient effect, progressive—rather than non-progressive jackpots—may increase EGM players’ perceived distance to the goal of ‘‘hitting the jackpot’’ as the jackpot value grows after each additional bet. From this perspective, the growing jackpot amount draws attention to the increasing number of historical bets that

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have been unsuccessful in winning the jackpot, making the goal appear more difficult to attain. Additionally, higher value prizes are associated with a lower perceived probability of winning, and a corresponding larger number of competing bettors. Moreover, progressive jackpots may make bettors more aware of their contributions to the jackpot, which may be properly seen to be most likely to benefit someone else when the jackpot goal appears distant and unachievable, adding to the power of demotivation. Are there situations in which either the rollover effect or the goal-gradient effect tends to be more dominant in affecting play motivation? In order to answer this question, we considered two other common dimensions of EGM jackpots: deterministic versus nondeterministic format, and varying prize amount. The contribution of knowledge activation theory in social psychology (Higgins 1996) leads us to expect that both features could influence the relative activation of rollover and goal-gradient effects on EGM play behavior.

Knowledge Activation Theory A classical topic in social psychology concerns what knowledge people activate and use when encountering or responding to a stimulus event (e.g., Krech and Crutchfield 1948). An important milestone in this line of research is the theoretical framework developed by Higgins (1996), who formulated a series of rules on knowledge activation, among which the ‘applicability rule’ is of direct relevance to the present study. The applicability rule suggests that the likelihood that some knowledge can be brought to mind from memory store is determined by the degree of overlapping between the features of the knowledge and the attended features of the stimulus being encountered, and that increased degree of overlapping leads to increased applicability of the knowledge to the stimulus and increased probability of the knowledge being activated and applied in decision making (Higgins 1996). For example, consider a patron of a gambling venue who is deciding which EGM she is going to play. If she happens to pay attention to the jackpot (vs. free-spin) features advertised in the venue, she may tend to rely on her knowledge about the jackpot (vs. freespin) features to choose the machine; as such knowledge is highly applicable to her attended aspect of the decision environments, and consequently highly accessible in her mind. Importantly, two critical criteria necessary to suffice the applicability rule lie in the

Deterministic

Fig. 1 Knowledge activation under different jackpot features

a) Goal-gradient effect

b) Goal-gradient & rollover effect

Non-progressive

Progressive

c) No effect

d) Rollover effect

Non-deterministic

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knowledge-stimulus overlapping and the attention drawn to the stimulus features. The subsequent section will specify how the EGM jackpot features in question can affect whether these criteria can be met.

Deterministic Versus Non-deterministic Jackpots: First Moderator Deterministic jackpots have a guaranteed payout after a fixed number of bets, which is determined in advance at random, but hidden from players. Non-deterministic jackpots, on the other hand, have a potential payout assessed at random with every bet. Therefore, deterministic jackpots incorporate a notion of goal-distance, where each bet decreases the number of bets remaining before the jackpot triggers. For non-deterministic jackpots, the probability of winning is constant over time (Rockloff and Hing 2013), and goal-distance does not apply. According to the overlapping criterion of the applicability rule (Higgins 1996), players’ knowledge about their distance to a jackpot, and therefore influence of the goal-gradient effect, would be activated only when the EGM features a deterministic rather than non-deterministic jackpot (Fig. 1, sections a & b). The rollover effect, however, is driven solely by knowledge regarding increasing jackpot size, and should therefore be applicable for any progressive jackpots, including those with deterministic or non-deterministic features (Fig. 1, sections b & d). As for EGMs with non-progressive and non-deterministic jackpots, there is little overlapping between their jackpot features and players’ knowledge about jackpot distance or jackpot growth. Hence, neither goalgradient nor rollover effect will be activated among players betting on such EGMs (Fig. 1, section c). As previously stated, the rollover effect and the goal-gradient effect tend to exert their motivating force in opposite directions. Under progressive jackpots, players’ gambling intensity should increase when the rollover effect is activated. The goal-gradient effect, on the other hand, can only strengthen betting intensity on EGMs featuring non-progressive jackpots. Furthermore, the activation of goal-gradient effect tends to demotivate players betting on progressive EGMs. Based on this theorization, it is expected that the rollover effect activated by progressive features should positively affect play behavior on non-deterministic EGMs (i.e., causing greater betting intensity in section d rather than c in Fig. 1). Moreover, the goal-gradient effect activated by deterministic features should lead to greater betting intensity on nonprogressive (Fig. 1, section a) rather than progressive EGMs (Fig. 1, section b), since the goal-gradient and rollover effects tend to cancel each other out in the latter situation.

Jackpot Size: Second Moderator Jackpot size may also moderate the influence of rollover and goal-gradient motivations on EGM play. As the size of a reward is positively related to its salience and its ability to attract attention (Eisenberger and Selbst 1994), a small (vs. large) reward can lead to less (vs. more) attention being focused on its relevant aspects (Easterbrook 1959). Hence, EGMs with small (vs. large) jackpots should reduce players’ attention to their jackpot features. Following the attention criterion of the applicability rule (Higgins 1996), both the solo and the combined jackpot effects proposed above would be less likely to occur among players betting on EGMs featuring small rather than large jackpots.

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Hypotheses Based on the theory outlined in the previous sections, the following hypotheses are proposed: H1 (rollover effect [ no effect): When players bet on EGMs featuring non-deterministic jackpots, their betting intensity will be greater if the jackpots are progressive rather than non-progressive. H2 (goal-gradient effect [ combined effect): When players bet on EGMs featuring deterministic jackpots, their betting intensity will be greater if the jackpots are non-progressive rather than progressive. H3 (size effect): than large.

Both effects in H1 and H2 will diminish if the jackpots are small rather

Purpose of the Experiment The present experiment was devised to investigate the hypothesized interactive effects of (non-) progressive jackpots, (non-) deterministic jackpots, and jackpot size on EGM gambling behavior. In particular, we wished to test which EGM jackpot configurations could lead to the hypothesized effects on aspects of play in the forms of bet size, betting speed and betting persistence, as well as on players’ subjective and physiological reactions.

Methods Participants One hundred and twenty-three (51 male, 72 female) participants, aged 18–82 (M = 50.4, SD = 16.4) completed the experiment following recruitment from newspaper-flyer advertisements in Bundaberg, Queensland Australia. The majority of the participants had a personal income level between $2001 and $799 per week (70.8 %) and an Australian cultural background (92.7 %). As calculated from the 9-item Problem Gambling Severity Index (PGSI, Ferris and Wynne 2001), 34.1, 21.1, and 10.6 % of the participants were lowrisk, moderate-risk, and problem gamblers. Furthermore, among the total participants, 93.5 % had gambling experiences before, 64.2 % had gambled on EGMs within the last 12 months, and 51.2 % regarded EGM as their preferred form of gambling. Please see Table 1 for more details on the experiment participants.

Computer Simulated EGM Programs Nine versions of a computer simulated EGM were created in Visual Basic for this study, including eight EGMs with different jackpot features and one EGM without a jackpot. When participants started those jackpot EGMs, they first encountered an instruction screen where the corresponding EGM’s jackpot features were explained. Then the program proceeded to the game interface, which displayed the game information on three areas; including a top area showing the present amount of the jackpot, a central area showing 1

Any money amount reported in this article is measured in Australian currency, unless otherwise stated.

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J Gambl Stud Table 1 Demographic and gambling characteristics of experiment participants (n = 123) Characteristic

n

%

Gender Male

51

41.5

Female

72

58.5

Age 30 or below

21

17.1

31–40

14

11.4

41–50

22

17.9

51–60

23

18.7

Above 60

43

35.0

Income Less than $80 per week

6

4.9

$80–$199 per week

15

12.2

$200–$499 per week

60

48.8

$500–$799 per week

27

22.0

$800–$1099 per week

6

4.9

$1100 and above per week

9

7.3

Cultural background Australian

114

92.7

English, New Zealand, South African, or German

8

6.5

Other

1

.8

PGSI 0 (No identifiable problems)

41

33.3

1–2 (Low-risk)

42

34.1

3–7 (Moderate-risk)

26

21.1

8 or More (problem gamblers)

13

10.6

Missing data

1

.8

115

93.5

Participants who had gambled on EGMs within the last 12 months

79

64.2

Participants who reported EGM as their preferred form of gambling

63

51.2

Participants who had gambling experiences before

three reels of symbols, and a bottom area showing betting options and results (see Fig. 2). When participants were playing, they chose the bet size (i.e., 25, 50, or 100 cents) and clicked on the ‘‘Spin!’’ button. At the conclusion of a spin, the reels rested on a threesymbol-combination outcome. The EGMs had the typical sounds associated with spinning reals, winning bets, and losing bets. As participants kept playing, the changes of the betting results and the jackpot amounts (for EGMs with progressive jackpots) were reflected on the screen after every single bet. The no-jackpot EGM was identical to the jackpot EGMs in most aspects, except the lack of jackpot instruction screen and jackpot information in the game. Furthermore, unbeknownst to the study participants, all EGMs were programmed to generate the same winning outcomes (i.e., 10 times the bet size) on five specific bets (i.e., 2nd, 6th, 8th, 13th, and 20th bet) and the same losing outcomes (i.e., 0 times the bet size) on all other bets. EGMs were programmed without any Jackpot wins. However, all

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Fig. 2 The game interface of a computer simulated EGM program in the experiment

participants who completed the experiment were put into an end-of-study lottery draw for a $500 jackpot prize.

Procedures Ethical clearance for this research project was gained through the Human Research Ethics Committee in the researchers’ university. Participants were given $20 upon arrival at their session as compensation for their time. After completing a brief questionnaire including demographic questions, gambling experience questions, and the Lie-Bet Scale (Johnson et al. 1988), participants were asked whether they would like to wager their $20 compensation on the EGM. Two people walked away with the $20 (an older male and female who arrived together). All other participants agreed to gamble with their $20. Once these participants agreed, the $20 was retrieved from them and loaded to the EGM for their subsequent play. Given the modest sample size, stratified random assignment based on participants’ gender, age, and Lie-Bet score was utilized to allocate participants to play the EGM in the different conditions (as described below). Prior to and during the EGM play, each participant had a finger sensor attached to his or her non-dominant hand, which measured a few physiological indices including skin conductance. After the EGM play was concluded (i.e., when participants indicated that they wanted to stop playing or when all credits had been lost), participants also completed a second questionnaire including

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questions on their attitudes toward the EGM utilized in the experiment, the Problem Gambling Severity Index (PGSI, Ferris and Wynne 2001), and questions on their personality and experiences related to gambling. Participants were also asked how truthful they had been in the survey. Except one participant who missed that question, all reported ‘‘completely truthful’’ or ‘‘mostly truthful’’.2 Finally, participants were debriefed and the remaining credits on the EGM (or $20 when the credits were less than $20) were paid back to them before they left.

Design The experiment was based on a 2 (progressive vs. non-progressive) 9 2 (deterministic vs. non-deterministic) 9 2 (small jackpot vs. large jackpot) factorial design with an additional no-jackpot control condition. Participants in the (non-) progressive and the (non-) deterministic conditions were informed of the mechanisms of the specific jackpot features on their EGM both verbally and with an instruction screen prior to play. In order to create authenticity feelings among our participants, we showed them the real jackpots they had the opportunity to win in the experiment. In the small jackpot conditions, they were shown a jar with $500 cash and told about the opportunity to win $500 as a cash jackpot. In the large jackpot conditions, we showed them a jar with 500 instant scratch-it tickets and told them about the opportunity to win instant scratch-it tickets for a $25,000 jackpot. Note that utilizing instant scratch-it tickets is the only credible way to appear to offer a high value jackpot. Since participants would doubt our willingness to offer a real cash prize of $25,000 from the university (as they should), offering instant scratch-it tickets, which we showed to them, has much greater credibility. A further advantage of this jackpot manipulation lies in the different winning likelihoods perceived by participants in the large and small jackpot conditions. Participants might feel that they needed to win twice (i.e., win the EGM jackpot first, then win the instant scratch-it ticket game) in the large jackpot conditions and only needed to win once (i.e., win the EGM jackpot) in the small jackpot conditions. Consequently, participants should perceive winning the large ticket jackpot as substantially less likely than the small cash one. Since we were testing the relative attraction of small but somewhat likely jackpots versus large but very improbable jackpots, the exact likelihoods that participants estimated were not important, as long as they perceived these differences. The instruction described each feature in functional terms without emotive words. As an example, the deterministic, progressive, large jackpot condition participants were told: ‘‘The $25,000 prize amount will be shown on the top of the screen once you begin. You’ll notice that the jackpot prize grows with every bet you make. The ticket jackpot will payout after a certain number of bets have been placed. The number of bets that must be made before the jackpot is triggered has been determined in advance and at random.’’ A design feature worth mentioning was the starting jackpot amounts displayed in the game interface of the simulated EGM programs. Specifically, $476, $500, $25,000, and $25,000 were displayed as the starting amounts for the small progressive, small nonprogressive, large progressive, and large non-progressive jackpot conditions. The intention of setting $476 for small progressive jackpot EGMs was to ensure the average jackpot size displayed on those EGMs to be around $500 during the play (i.e., similar to the jackpot size displayed on the small non-progressive jackpot EGMs). Since all EGMs were programmed 2

The participant who missed that question was included in the analysis, since excluding the participant didn’t change the substance of the results.

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to generate winning outcomes on five out of the first 20 bets and losing outcomes on all other bets, participants experienced on average one win every four bets in their first 20 bets. Then when they placed another four bets (i.e., had placed their 24th bet) and did not win, they should start feeling a decreasing chance of winning. Hence, the 24th bet represented a critical turning point in the middle of participants’ play. They would keep playing for a while until they finally realized the inevitability of losing and stopped playing. As for the EGMs with the small progressive jackpots, their starting jackpot of $476 would grow to $500 after 24 bets and keep growing for a while until the betting ended, leading to an average jackpot size around $500 during the entire play. We kept $25,000 as the starting amount for large progressive jackpots since that was a much larger figure and the growth of the jackpot would not change its average amount to dramatically different from $25,000.

Results Data Analysis The primary response variables of interest included the behavioral outcomes of average bet size, betting speed (bets per minute), persistence (number of bets), the self-reported attitudes toward the EGM utilized in the experiment (a three-item, six-point Likert scale), and the physiological arousal measured by skin conductance changes due to the EGM play. Each outcome was analyzed with an ANCOVA model using (non-) progressive feature, (non-) deterministic feature, and jackpot size as the primary predictor variables in a crossed design. Gender, age, personal income, and PGSI (Ferris and Wynne 2001) were entered as covariates, as each of these variables could play certain roles in influencing participants’ gambling behavior (e.g., Browne et al. 2014; Johansson et al. 2009). Table 2 ANCOVA predicting average bet size from (non-) progressive feature, (non-) deterministic feature, and jackpot size (large or small) Variable

df

MS

gp2

F

Progressive

1

663.23

1.90

Deterministic

1

318.23

.91

.02 .01

Size

1

328.59

.94

.01

Progressive 9 deterministic

1

1326.98

3.80

.04

Progressive 9 size

1

282.78

.81

.01

Deterministic 9 size

1

484.57

1.39

.01

Progressive 9 deterministic 9 size

1

1512.00

4.33*

.04

Gender

1

2.38

.01

.00

Age

1

368.88

1.06

.01

Personal income

1

121.65

.35

.00

PGSI

1

4972.64

14.24***

.13

Error

98

349.15

Total

110

* p \ .05; **p \ .01; ***p \ .001

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Treatment of Missing Data The missing data were treated through two ways. Firstly, minor missing data were found for less than 5 % of the total participants on their EGM attitudes or PGSI items. These missing data were replaced through the Expectation Maximization (EM) technique. Secondly, due to equipment/recording failure, data on 22 participants’ (i.e., about 18 % of the total participants) skin conductance changes were also missing. These participants were excluded from any analysis involving the physiological arousal (i.e., listwise deletion).

Average Bet Size The first ANCOVA model used average bet size as the outcome, and showed a significant main effect of PGSI (F(1, 98) = 14.24, p \ .001, g2p = .13). A follow-up partial correlation analysis was conducted between average bet size and PGSI, controlling for gender, age, and personal income. The result demonstrated that players more at risk of gambling problems tended to bet more on EGMs with jackpots (rp = .35, p \ .001). Consistent with our hypotheses, the ANCOVA model also demonstrated a significant three-way interaction effect of (non-) progressive feature, (non-) deterministic feature, and jackpot size on participants’ average bet size (F(1, 98) = 4.33, p \ .05, g2p = .04). The results of this ANCOVA model are listed in Table 2, and the three-way interaction is illustrated in Fig. 3. To further explore the three-way interaction, we decomposed the interaction for participants in large and small jackpot conditions. When the jackpot was large (Fig. 3, Panel a), there was a significant two-way interaction between (non-) progressive and (non-) deterministic features (F(1,48) = 8.16, p \ .01, g2p = .15). Simple effect revealed that participants in large deterministic jackpot conditions placed higher bets on the EGM with non-progressive (M = 54.93 cents, SD = 23.33) rather than progressive jackpot (M = 38.04 cents, SD = 12.83, F(1, 48) = 5.08, p \ .05, g2p = .10). In contrast, participants in large non-deterministic jackpot conditions, bet on average more on the EGM with

a

Progressive

Non-progressive

b

60

Non-progressive

55

53.79

50 45

41.59

40

38.04

35 30

Average bet size (cents)

60 54.93

Average bet size (cents)

Progressive

55 50

52.71 47.15 43.29

45 40

34.70

35 30

Deterministic

Non-deterministic

Deterministic

Non-deterministic

Fig. 3 Average bet size by (non-) progressive feature, (non-) deterministic feature, and jackpot size (a, b). a Large jackpot condition. b Small jackpot condition

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progressive (M = 53.79 cents, SD = 26.82) rather than non-progressive jackpot (M = 41.59 cents, SD = 14.10), although the simple effect fell short of significance (F(1, 48) = 3.32, p \ .10, g2p = .07). Figure 3 Panel b shows the average bet size on small jackpot EGMs in different conditions. Only the main effect of PGSI was significant (F(1, 46) = 18.11, p \ .001, g2p = .28), and the PGSI score was positively correlated with the average amount of bet placed on small jackpot EGMs after controlling for gender, age, and personal income (rp = .53, p \ .001). None of the other main or interaction effects were significant (p’s [ .05). Furthermore, both simple effects in small deterministic and non-deterministic jackpot conditions proved non-significant (p’s [ .05).

Speed of Betting (Bets per Minute) The second ANCOVA model, using the speed of betting as the outcome, found no significant effects of the jackpot features, the jackpot size, or their interactions (p’s [ .05). Among the covariates, only the effect of PGSI was significant (F(1, 98) = 4.10, p \ .05,g2p = .04). However, the partial correlation between bets per minute and PGSI did not reach significance, after controlling for gender, age, and personal income (rp = -.18, p [ .05).

Persistence (Number of Bets) The third ANCOVA model examined the total number of bets placed by participants as the response variable. Since all bets were programmed as losses past the 20th bet, and participants across all jackpot conditions placed on average 79.32 bets (SD = 32.86, only six participants placed 20 or fewer bets on jackpot EGMs), the number of bets placed by participants essentially reflected their persistence while losing. According to the results of this ANCOVA model, the jackpot features or size did not reliably predict continued play, nor did any interactions or covariates (p’s [ .05). Removing those six participants who placed 20 or fewer bets did not change these analysis results.

EGM Attitudes Participants’ attitudes toward the EGM they played were measured by a three-item, sixpoint Likert scale (the Poker Machine was enjoyable, the Poker Machine was exciting, I would like to play the Poker Machine again; 1 = strongly disagree, 6 = strongly agree). The average of the three items was calculated to form an EGM attitudes composite (a = .79), which was entered into the fourth ANCOVA model as the response variable. The results showed no significant effects of the jackpot features, the jackpot size, their interactions, or any covariates on the EGM attitudes (p’s [ .05).

Physiological Arousal A fifth ANCOVA model used as the response variable participants’ physiological arousal due to the EGM play, which was measured by the changes in skin conductance (i.e., the differences between the average experimental skin conductance during play and the average baseline skin conductance prior to play). None of the jackpot features, the jackpot size, or their interactions had any significant effects on skin conductance changes

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(p’s [ .05). Among the covariates, only the effect of gender was significant (F(1, 78) = 5.93, p \ .05, g2p = .07), with male participants showing larger skin conductance increases than females due to playing the jackpot EGMs, even after controlling for age, personal income, and PGSI (rp = .23, p \ .05).

No-Jackpot Condition Each of the eight jackpot conditions in the factorial design was compared with the nojackpot condition through ANCOVA models using the conditions as the primary predictor variables and gender, age, personal income, and PGSI as the covariates. None of the eight jackpot conditions were significantly different from the no-jackpot control condition for average bet size (all p’s [ .05), bets per minute (all p’s [ .05), number of bets (all p’s [ .05), EGM attitudes (all p’s [ .05), or physiological arousal (all p’s [ .05).

Discussion The present experiment sought to examine the effects of (non-) progressive jackpots on EGM playing behavior, and how (non-) deterministic jackpots and jackpot size may moderate such effects. In a crossed-design, the results revealed a significant interaction between the (non-) progressive feature, (non-) deterministic feature, and jackpot size on participants’ average bet size on the EGM. In particular, the largest bets were made on high jackpot machines ($25,000) that were represented as deterministic (i.e., a jackpot payoff after a fixed number of bets, which is determined in advance at random) and non-progressive (i.e., payoff at a fixed jackpot amount). These machines may have appeared more valuable because of the dominance of goal-gradient effect. The incremental bets may have encouraged larger bet sizes as the players felt they were drawing nearer to an inevitable payoff event. That is, each bet places the gambler closer to the goal of winning the jackpot prize. The experiment demonstrates that intensity of betting, at least by measure of bet size, is largest in the presence of this type of jackpot. In fact, the average bet size for the large, deterministic and non-progressive jackpot (M = 54.93 cents, SD = 23.33) was 20.3 % higher than the bet-size average for all studied jackpot feature combinations (M = 45.67 cents, SD = 20.33). Importantly, large jackpots that were non-deterministic (potential payoff assessed at random with each bet) and progressive (each bet adds to the jackpot prize) also promoted high average bet sizes. This is a common configuration for jackpots in gaming venues, and in this condition demonstrated bet-sizes (M = 53.79 cents, SD = 26.82) were 17.8 % higher than the average for all studied jackpot configurations (M = 45.67 cents, SD = 20.33). This jackpot configuration may be attractive due to the rollover effect that was demonstrated to make rollover lottery prizes more attractive (Beenstock and Haitovsky 2001; Farrell et al. 1999; Rogers 1998; Rogers and Webley 2001). Players may justify the placing of large bets that add to the large jackpot prize, as they can potentially be later recouped through attaining the jackpot prize. Furthermore, jackpot size affected EGM players’ susceptibility to both rollover and goal-gradient effects. These effects were observed on high-jackpot machines, but they disappeared in low-jackpot conditions. Hence, all three findings mentioned above were consistent with the hypotheses formulated through the knowledge activation theory.

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In recent years, the knowledge activation theory has been increasingly utilized in explaining various addiction phenomena such as smoking or alcohol use (e.g., Cervone et al. 2007; Friedman et al. 2005, 2009). The present study, to the best of our knowledge, is among the first gambling research that builds upon the knowledge activation theory. This critical theory may provide a useful framework for examining other important gambling phenomena in the future. In addition to the results that supported the key hypotheses, the present study also demonstrated that players who reported higher PGSI scores tended to bet more on EGMs with jackpots, and that males experienced larger physiological arousal than females due to playing the jackpot EGMs. These findings also add to the growing gambling literature on PGSI or gender effects (e.g., Browne et al. 2014; Johansson et al. 2009).

Limitations As is true for all lab-based experimental studies, there are concerns about the external validity of the results. The artificial environment of the lab, as well as the way in which the jackpots were described, may not be entirely true to the information players receive in a real venue. Nevertheless, the jackpots were described in simple and functional language, and these descriptions produced reliable effects on average bet size. We take a view on the importance of experimental realism over mundane realism, where it is important to understand the psychological constraints and contingencies that operate in jackpot EGMs (offering real money prizes), rather than attempting to faithfully recreate every detail of the gambling environment.

Conclusion This experiment provided some evidence that common jackpot configurations are associated with higher average bet sizes, which are one component of gambling intensity. That such jackpot configurations are relatively common in venues is likely a consequence of the natural evolution of EGMs, where only the most popular and profitable machines survive competition on the gaming floor. Acknowledgments This research was fully funded by Gambling Research Australia, a partnership between the Commonwealth, State and Territory Governments. A previous version of this paper has been submitted and published as part of a research report commissioned by Gambling Research Australia. Conflict of interest No conflicts of interest are declared.

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