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Ke Zhang Department of Psychology, Centre for Gambling Research at UBC, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada

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Jason D. Rights The Rights Lab, Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada

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Xiaolei Deng Department of Psychology, Centre for Gambling Research at UBC, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada

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Tilman Lesch Department of Psychology, Centre for Gambling Research at UBC, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada

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Luke Clark Department of Psychology, Centre for Gambling Research at UBC, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada
Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada

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Abstract

Background and aims

This study characterized chasing behaviour as the time to return to an online gambling website after a losing or a winning visit.

Methods

We analyzed a naturalistic dataset from an eCasino (PlayNow.com, the provincial platform for British Columbia, Canada), comprising 1,909,681 sessions from 15,544 individuals. Analyses distinguished sessions on slot machines, blackjack, roulette, video poker, probability games, or mixed-category sessions.

Results

Overall, gamblers on most games returned more slowly as a function of the prior loss, and more quickly as a function of the prior win. Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machines, but roulette was associated with shorter intervals to return (b = −0.13, p < 0.001). Similarly, win chasing did not vary across slot machines, blackjack, probability games, and video poker, but roulette (b = −0.08, p < 0.001) and mixed (b = −0.02, p = 0.009) sessions were associated with shorter intervals.

Discussion and conclusions

The average behavioural patterns provide limited evidence for loss chasing but clearly indicate win chasing. Although slot machines are commonly considered a high-risk product, roulette in our analyses was associated with the greatest chasing intensities.

Abstract

Background and aims

This study characterized chasing behaviour as the time to return to an online gambling website after a losing or a winning visit.

Methods

We analyzed a naturalistic dataset from an eCasino (PlayNow.com, the provincial platform for British Columbia, Canada), comprising 1,909,681 sessions from 15,544 individuals. Analyses distinguished sessions on slot machines, blackjack, roulette, video poker, probability games, or mixed-category sessions.

Results

Overall, gamblers on most games returned more slowly as a function of the prior loss, and more quickly as a function of the prior win. Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machines, but roulette was associated with shorter intervals to return (b = −0.13, p < 0.001). Similarly, win chasing did not vary across slot machines, blackjack, probability games, and video poker, but roulette (b = −0.08, p < 0.001) and mixed (b = −0.02, p = 0.009) sessions were associated with shorter intervals.

Discussion and conclusions

The average behavioural patterns provide limited evidence for loss chasing but clearly indicate win chasing. Although slot machines are commonly considered a high-risk product, roulette in our analyses was associated with the greatest chasing intensities.

Introduction

Chasing is widely considered one of the hallmarks of disordered gambling (Lesieur, 1979; Zhang & Clark, 2020). Broadly speaking, it refers to the persistence or escalation of betting in an effort to recover the gambler's debts. From this perspective, chasing is conventionally seen as a response to losing. However, it has also been noted that wins can drive chasing, and exacerbate gambling problems (Delfabbro, King, & Griffiths, 2014; O'Connor & Dickerson, 2003; Young, Wohl, Matheson, Baumann, & Anisman, 2008). Given the house edge (negative expectancy) of commercial gambling products, chasing after wins is likely to increase cumulative losses, as well as providing further opportunity for habit formation (Ferrari, Limbrick-Oldfield, & Clark, 2022). In past work, win chasing was associated with impaired control over gambling (O'Connor & Dickerson, 2003), and high-risk gamblers reported greater gambling desire after wins compared to losses (Young et al., 2008). Hence, examining chasing after wins is also important for understanding the development of gambling problems. In the DSM-5, the chasing item specifically refers to a gambler who “often returns another day to get even” (section 312.31; American Psychiatric Association et al., 2013). Indeed, this ‘between-session chasing’ is the most frequently endorsed item among the DSM items for Gambling Disorder (Sleczka & Romild, 2021; Toce-Gerstein, Gerstein, & Volberg, 2003).

This study primarily aimed to characterize between-session chasing in a large ‘behavioural tracking’ dataset of online gamblers, examining the time interval between consecutive sessions, as a function of both the amounts lost (loss chasing) and amounts won (win chasing). In light of the close links between chasing and gambling problems, the study also compared chasing intensities across gambling product types, to derive insight into the potential risks associated with different gambling forms.

Most research on between-session chasing to date used retrospective surveys (Gainsbury, Suhonen, & Saastamoinen, 2014; Lesieur, 1979; O'Connor & Dickerson, 2003; Sleczka & Romild, 2021; Temcheff, Paskus, Potenza, & Derevensky, 2016). Temcheff et al. (2016) surveyed 8,674 college women athletes, only the chasing item could distinguish those with and without gambling problems. A Swedish longitudinal survey highlighted that the endorsement of chasing is a stable predictor of the risk of transitioning to more severe gambling problems over 5 years (Sleczka & Romild, 2021). However, self-report measures are subject to a number of biases (Braverman, Tom, & Shaffer, 2014). A small number of studies have examined chasing using field data from (land-based) casinos (Flepp, Meier, & Franck, 2021; Forrest & McHale, 2016; Kainulainen, 2020; Narayanan & Manchanda, 2012; Wardle, Excell, Ireland, Ilic, & Sharman, 2014). Measuring the time interval between visits as an index of chasing, these studies found that gamblers took longer to return after a losing session, and shorter intervals to return after winning (Forrest & McHale, 2016; Kainulainen, 2020; Narayanan & Manchanda, 2012). Chasing might also be expressed as increasing the overall bet amount over successive visits. Tracking slot machine engagement in a Swiss casino, a small loss (i.e., below USD 188) did not change the subsequent bet amount, whereas a larger loss reduced the subsequent bet amount (Flepp et al., 2021). Thus, the aggregate profile in land-based gambling indicates win chasing, but not loss chasing.

This study sought to capture between-session chasing in the online environment, which presents a direct means of linking gamblers' accounts with their behaviour, compared to tracking gamblers in land-based gambling venues (Deng, Lesch, & Clark, 2019). We used a dataset from the PlayNow.com eCasino, the provincial gambling platform in British Columbia (BC), Canada. Across many jurisdictions, online gamblers and land-based gamblers often have different demographic backgrounds (Gainsbury, Wood, Russell, Hing, & Blaszczynski, 2012, 2015). Online gamblers also have a greater risk of disordered gambling than land-based gamblers (e.g., Papineau et al., 2018). In a 2020 prevalence survey in BC, Canada, 24% of online gamblers were classified as high-risk for gambling problems on the Problem Gambling Severity Index (Ipsos & Strategic Science, 2020). Prior studies examining European online gambling datasets have considered the operationalization of chasing behaviour (Auer & Griffiths, 2022; Challet-Bouju et al., 2020; Perrot, Hardouin, Grall-Bronnec, & Challet-Bouju, 2018). Challet-Bouju et al. (2020) and Perrot et al. (2018) inferred chasing from successive money deposits within a short period of time or a deposit within 1 h after placing a bet. Auer and Griffiths (2022) defined five alternative metrics of loss chasing, including an ‘across days’ chasing measure based on the correlation between the amount lost and the subsequent amount bet, restricted to pairs of consecutive gambling days. In their study, another metric reflecting the percentage of sessions with more than one financial deposit appeared most sensitive to chasing (Auer & Griffiths, 2022). We note these studies did not quantify chasing intensities in relation to winning outcomes.

Our further objective was to examine chasing differences between gambling product categories. Past behavioural tracking studies rarely distinguished games (Forrest & McHale, 2016; Wardle et al., 2014). Some studies have focussed on single gambling products (Flepp et al., 2021; Kainulainen, 2020; Narayanan & Manchanda, 2012), where it is unclear how observed patterns would generalise to other games. Different games vary in their structural characteristics (Griffiths, 1993), in ways that are likely to influence chasing and the potential for disordered gambling. For example, slot machines have a fast and continuous speed of play, and intense audiovisual stimulation, which foster psychological states of immersion (Dixon et al., 2014, 2018; Dowling et al., 2017; Murch & Clark, 2021). In a survey, slot machine gamblers were more likely to “keep playing to try to win back their losses”, compared to roulette and blackjack gamblers (Gainsbury et al., 2014). Slot machines were the most popular game type in our dataset, representing 57% of sessions, and they were used as the reference category.

We hypothesized that among online gamblers, the time interval between sessions would decrease as a function of the amount lost in the prior session (H1: loss chasing), and would decrease as a function of the amount won in the prior session (H2: win chasing). Furthermore, our analyses distinguished game types of slot machines, blackjack, roulette, video poker, and probability games (e.g., pachinko, reactors), and we hypothesized that slot machine sessions would be associated with the shortest between-session intervals, as a function of the amount lost (H3: loss chasing by game type) and amount won (H4: win chasing by game type) in the prior session.

Methods

Data overview

The study used a behavioural tracking dataset obtained from the eCasino section of PlayNow.com, the provincial gambling platform for BC, Canada, which is restricted to BC residents. The website requires customers to create a user account, which allows the website to track individual gamblers' bet-by-bet behaviour with timestamps. The dataset was de-identified by the British Columbia Lottery Corporation (BCLC) Data Analytics team by randomly assigning each gambler a unique ID. The dataset spans from 2014-10-01 to 2015-08-31, comprising 527,015,222 individual bets placed by 29,964 gamblers. During the time period under scrutiny, the eCasino contained five game categories, which further comprised 240 specific products: slot machines (n = 195 individual products), roulette (n = 8), blackjack (n = 9), video poker (n = 13), and probability games (n = 15).

Our analysis code is on https://osf.io/dcv65/. We began by aggregating the bet-by-bet data into sessions. We define a session as a period of activity that begins with the first bet and ends with the last bet before the gambler is inactive for 30 min, in which case they are logged off automatically. This time interval reflects the gambler's log-on and log-off periods on the website. During a session, gamblers can play one game type (79% of sessions), or place bets across multiple game categories, termed as “mixed” sessions (21% of sessions). Thus, the aggregated session data comprised six types: slot machines, roulette, blackjack, video poker, probability games, and mixed sessions. Slot machines were the most popular product type in the eCasino (57% of sessions, see Table 1).

Table 1.

Descriptive statistics of session data by game type

GameSession count (%)Gambler count (%)
Slot machines1,081,499 (56.63%)12,285 (79.03%)
Mixed sessions412,858 (21.62%)12,673 (81.53%)
Probability games184,933 (9.68%)5,870 (37.36%)
Blackjack141,796 (7.43%)4,420 (28.44%)
Video poker48,691 (2.55%)1,264 (8.13%)
Roulette39,904 (2.09%)2,402 (15.45%)
Outcome (Canadian $)
GameMedianMeanSD
Mixed sessions−35.04−116.58874.02
Slot machines−26.95−97.58633.69
Blackjack−19.96−104.39399.74
Video poker−17.92−99.68642.14
Probability games−12.10−43.70784.11
Roulette−10.00−79.58355.21
Time to return (hours)
GameMedianMeanSD
Roulette144.861,111.264,193.87
Blackjack139.93913.433,414.25
Slot machines127.05529.912,081.96
Mixed sessions117.51524.842,230.79
Video poker100.26435.612,057.99
Probability games86.29407.461,784.55

Note: Gambler count is non-exclusive in game played. For example, the same person can have both slot machine sessions and probability game sessions.

The analysis included gamblers who visited the eCasino more than five times. This represents an arbitrary threshold (although see Finkenwirth, MacDonald, Deng, Lesch, & Clark, 2021; Percy, França, Dragičević, & d'Avila Garcez, 2016), but between-session chasing inherently requires more than one session, and it is challenging to use data from gamblers with low levels of activity in modelling behavioural markers of high-risk gambling. We excluded a further gambler who was an outlier in terms of high bet frequency. The analytical sample included 1,909,681 sessions from 15,544 gamblers.

Statistical analysis

Variables

The analysis aimed to measure how prior session outcomes impact the following time to return to the eCasino. The time interval (Time to Return, expressed in hours) between the end of the prior session and the start of the next session is the dependent variable. A faster time to return reflects a greater chasing tendency. We applied a natural log transform on Time to Return because of its positive skewed nature in distribution, with most gamblers returned in modest time intervals, but some gamblers returned to the eCasino after extremely long intervals. The key predictors were:

  1. Game: game types included slot machines, probability games, blackjack, video poker, roulette, and mixed sessions.
  2. Outcome Dummy: a dummy variable indicated the outcome valence - win or loss, which might lead to differential chasing patterns. A negative net balance refers to a net loss. A positive net balance, which also includes zeros as ‘break-even’ sessions, refers to a net win. We re-parametrized the model in two ways: we used loss as Outcome Dummy reference (loss = 0, win = 1) to interpret loss chasing, and win as Outcome Dummy reference (win = 0, loss = 1) to interpret win chasing.
  3. Outcome: the absolute values of net outcome, which is the total paid amount minus the winning amount in a session (see also Leino et al., 2016). Outcome values were standardized with respect to individual gamblers' mean of loss or win amounts; depending on Outcome Dummy, a value of zero indicates an average loss (or win) amount for that gambler over the 11-month data. This standardized within person method is in line with our research interest in individual gamblers' responses to the prior outcome when winning or losing more than their personal average.

Additionally, we covaried for other time-related nuisance variables that could impact the timing of a new session:

  1. Session Order: the order of the current session on any given day, included in analyses as a covariate. We tested models that had linear or quadratic effects of raw or (natural) log-transformed session order, and ultimately used a linear effect of log-transformed Session Order as it yielded the best fit via BIC.
  2. Start hour: we anchored time of the day by the session's start time and classify it into the following periods: morning (12:00 AM–8:59 AM), early daytime (9:00 AM–2:59 PM), late daytime (3:00 PM–6:59 PM), night (7:00 PM–11:59 PM).
  3. Weekends: a dummy variable indicating whether the session occurred on a weekday (No = 0) or a weekend (Yes = 1).
  4. BC statutory holidays: a dummy variable indicating whether the session occurred on a holiday (Yes = 1) or not (No = 0).

Analysis

Analyses used R (Version 4.1.1; R Core Team, 2021) and Python (Version 3.6). The data structure is in two-level hierarchical: level 1 contains the variables we introduced earlier, which are clustered by gambler ID at level 2. Since there could be random differences in sessional variables between gamblers, we used multilevel linear modelling. An alternative method to analyze time-to-event data is survival analysis, which estimates the probability of a target event occurring. We considered this approach but our dataset comprises multiple events per participant, and our hypothesis refers to the time interval between events, as a continuous dependent variable, which is better suited to the multilevel linear model.

To test our hypothesis that prior loss and game impacted Time to Return to the eCasino, we specified our model as follows, and for simplicity, the model equation does not represent all of dummy codes for all the categorical variables with more than 2 categories:
LogTimetoReturnij=β0j+β1jOutcomeij+β2jOutcomeDummyij+β3jGameij+β4jOutcomeij*OutcomeDummyij+β5jOutcomeij*Gameij+β6jOutcomeDummyij*Gameij+β7jOutcomeij*OutcomeDummyij*Gameij+β8jBCholidayij+β9jStarthourij+β10jWeekendij+β11jLogSessionOrderij+eij,
let i be the last session, j indicates Gambler ID. For Game, slot machines were set as the reference (slot machines = 0). At level 2 of random factors, the model allowed random intercepts and random slope of Outcomeij*OutcomeDummyij varying by GamblerID, which allowed the degree of win and loss chasing to differ randomly across persons. All other slopes were fixed, as attempting to include more random slopes led to non-converged or improper solutions.

We used the lme4 package in R (Bates, Mächler, Bolker, & Walker, 2015) for modelling. We use p = 0.01 as the cut-off alpha level to determine statistical significance given our large data size. Log-transforming the dependent variable makes the model estimates not intuitive for interpretation, thus we transformed estimated coefficients to the percentage of changes using 100*(eb1).

Ethics

The study is an analysis on the de-identified secondary gambling data, which were given to the Centre by the BCLC under a Non-Disclosure Agreement that does not allow data sharing or any reporting of information from individual users. The University of British Columbia's Behavioural Research Ethics Board gave ethical approval to store and analyse the secondary dataset.

Results

Descriptive results

We report medians due to the heavy skewness of the data (Table 1). On average, gamblers lost the most (Median = $-35.04) in mixed sessions. Slot machines – as the most played game category - ranked second (Median = $-26.95). Gamblers lost the least on roulette (Median = $-10.00). In terms of the time to return, gamblers returned the slowest overall after roulette sessions (Median = 144.86 h) and returned the fastest after probability game sessions (Median = 86.29 h). The time to return was intermediate for slot machine sessions (Median = 127.05 h).

Time to return as a function of prior session outcome

Figure 1 shows the time to return as a function of the prior loss and win. The x-axis depicts the standardized loss (win) amount based on the distribution of outcomes for that individual gambler over the data window. Zero constitutes their personal average loss (win) amount, and a positive outcome indicates the gambler lost (won) more than their personal average. A downward slope indicates that gamblers return faster as a function of larger prior loss (win), i.e. chasing, whereas an upward slope indicates that gamblers return slower after larger losses (wins).

Fig. 1.
Fig. 1.

Between-session chasing by game type

Note: The shaded area is standard errors.

Citation: Journal of Behavioral Addictions 2024; 10.1556/2006.2024.00022

After losing the prior session, gamblers returned more slowly across all game types, with the exception of roulette (Table 2). For a standard deviation increase in the prior session net loss, average gamblers playing slot machine sessions were estimated to took 8.59% longer to return to the platform (Table 3a). Compared to slot machines, blackjack (p = 0.740), probability games (p = 0.157), video poker (p = 0.103) on average did not differ significantly in the loss slopes. Mixed sessions on average were marginally faster in comparison to slot machine sessions (p = 0.015), but this was not significant at the conservative threshold. For roulette, average gamblers were estimated to return faster overall as a function of the amount lost: a standard deviation increase in the prior loss was estimated to reduce the time to return by approximately 4.78%.1 The downward slope for roulette was significantly less steep than the upward slope for slot machines (p < 0.001), indicating that gamblers in roulette sessions were less sensitive to the prior loss than gamblers in slot machine sessions. The overall model yielded an intraclass correlation coefficient (ICC) of 0.36, a conditional R2 of 0.39, and a marginal R2 of 0.05, indicating that fixed and random effects together explained 39% of the variance in time to return, and fixed effect alone explained 5% of the variance.

Table 2.

Summary of between-session across game types

Time to return (hours)
Loss chasingWin chasing
Slot machines
Mixed sessions✓*
Roulette✓*✓*
Blackjack
Probability games
Video poker

Note: ‘✓’ indicates a numerical effect in the direction of chasing; ‘✗’ indicates a directional absence of chasing under the measurement. ‘*’ indicates a significant difference (p < 0.01) in chasing relative to slot machines as the reference category.

Table 3.

Between-session chasing regression results

a: the reference of loss
Termestimatestd.errorstatisticdfp valueconf.lowconf.hightransformed estimate
(Intercept)5.780.01568.5518,612.91<0.0015.765.8032,420.96
Outcome0.080.0024.6110,311.00<0.0010.080.098.59
Outcome Dummy (Win = 1)−0.700.00−198.741,881,406.20<0.001−0.70−0.69−50.23
Blackjack0.140.0113.151,204,495.17<0.0010.120.1614.52
Probability−0.140.01−21.901,799,009.01<0.001−0.15−0.13−13.13
Mixed−0.010.00−2.481,894,476.100.013−0.020.00−1.03
Roulette0.310.0219.091,212,709.450.0000.280.3435.96
Video poker0.010.010.541,583,210.180.586−0.020.040.82
Outcome * Outcome Dummy (Win = 1)−0.150.00−30.517,102.44<0.001−0.16−0.14−14.06
Outcome * Game (Loss chasing by Game)
Blackjack0.000.01−0.3336,217.270.740−0.020.01−0.30
Probability0.010.011.42166,567.990.1570.000.021.02
Mixed0.010.002.44190,163.820.0150.000.020.97
Roulette−0.130.02−8.4167,375.14<0.001−0.16−0.10−12.31
Video poker0.020.021.6351,664.050.103−0.010.052.51
Outcome Dummy (Win = 1) * Game
 Blackjack−0.150.01−16.181,896,136.80<0.001−0.17−0.14−14.36
 Probability0.100.0110.581,545,180.88<0.0010.080.1210.72
 Mixed0.060.018.591,885,785.52<0.0010.040.075.88
 Roulette−0.290.02−16.551,837,680.26<0.001−0.33−0.26−25.50
 Video poker0.040.022.691,845,311.570.0070.010.074.42
Outcome * Outcome Dummy (Win = 1) * Game
 Blackjack−0.010.01−0.7114,101.800.477−0.030.02−0.90
 Probability−0.020.01−1.6019,349.360.109−0.050.01−2.24
 Mixed−0.030.01−3.6030,746.84<0.001−0.04−0.01−2.60
 Roulette0.050.022.5327,129.200.0110.010.105.59
 Video poker0.030.021.129,090.020.261−0.020.072.74
Time nuisance
 BC holidays (Yes = 1)0.046900.017.271,889,227.59<0.0010.030.064.80
 Early daytime 9am–2pm−0.081590.00−22.451,895,871.95<0.001−0.09−0.07−7.84
 Late daytime 3pm–6pm0.029360.007.731,897,520.24<0.0010.020.042.98
 Night0.528390.00140.191,900,153.44<0.0010.520.5469.62
 Weekend (Yes = 1)0.047410.0018.781,892,680.48<0.0010.040.054.86
 Log(Session count)−0.229190.00−93.331,897,962.69<0.001−0.23−0.22−20.48
b: the reference of win
Termestimatestd.errorstatisticdfp valueconf.lowconf.hightransformed estimate
(Intercept)5.090.01485.5720,924.79<0.0015.075.1116,084.84
Outcome−0.070.00−16.076,344.69<0.001−0.08−0.06−6.68
Outcome Dummy (Loss = 1)0.700.00198.741,881,406.18<0.0010.690.70100.93
Blackjack−0.020.01−1.791,262,439.640.073−0.040.00−1.92
Probability−0.040.01−4.111,396,047.74<0.001−0.06−0.02−3.82
Mixed0.050.017.451,886,143.90<0.0010.030.064.79
Roulette0.010.020.751,305,816.860.452−0.020.051.29
Video poker0.050.022.861,691,066.32<0.0010.020.095.27
Outcome * Outcome Dummy (Loss = 1)0.150.0030.517,102.47<0.0010.140.1616.36
Outcome * Game (Win chasing by Game)
Blackjack−0.010.01−1.229,578.000.222−0.030.01−1.20
Probability−0.010.01−0.9915,655.750.324−0.040.01−1.24
Mixed−0.020.01−2.6323,893.850.009−0.030.00−1.66
Roulette−0.080.02−4.7514,347.93<0.001−0.11−0.05−7.40
Video poker0.050.022.517,325.550.0120.010.095.32
Outcome Dummy (Loss = 1) * Game
 Blackjack0.150.0116.181,896,136.83<0.0010.140.1716.76
 Probability−0.100.01−10.581,545,183.59<0.001−0.12−0.08−9.68
 Mixed−0.060.01−8.591,885,785.57<0.001−0.07−0.04−5.55
 Roulette0.290.0216.551,837,680.76<0.0010.260.3334.23
 Video poker−0.040.02−2.691,845,312.000.007−0.07−0.01−4.23
Outcome * Outcome Dummy (Loss = 1) * Game
 Blackjack0.010.010.7114,101.900.477−0.020.030.91
 Probability0.020.011.6019,349.580.109−0.010.052.29
 Mixed0.030.013.6030,747.18<0.0010.010.042.67
 Roulette−0.050.02−2.5327,129.380.011−0.10−0.01−5.30
 Video poker−0.030.02−1.129,090.110.261−0.070.02−2.66
Time nuisance
 BC holidays (Yes = 1)0.050.017.271,889,227.57<0.0010.030.064.80
 Early daytime 9am–2pm−0.080.00−22.451,895,871.93<0.001−0.09−0.07−7.84
 Late daytime 3pm–6pm0.030.007.731,897,520.23<0.0010.020.042.98
 Night0.530.00140.191,900,153.43<0.0010.520.5469.62
 Weekend (Yes = 1)0.050.0018.781,892,680.47<0.0010.040.054.86
 Log(Session count)−0.230.00−93.331,897,962.67<0.001−0.23−0.22−20.48
Conditional R-squared0.39
Marginal R-squared0.05

Note: panel (a) as losses as the reference level, and panel (b) has wins as the reference level.

For the analyses of win chasing, average gamblers were estimated to return faster as a function of the prior amount won across all game types, thus indicating a propensity for win chasing (see Fig. 1, Table 3b). For a standard deviation increase in the prior win, average slot machine gamblers were estimated to take 6.68% less time to return. The rates of win chasing differed by game types: for mixed sessions and roulette, the slope for win chasing was on average significantly steeper than for slot machines (mixed, p = 0.009; roulette, p < 0.001). For mixed sessions, a one standard deviation increase was associated with an average 8.23% shorter time to return, and for roulette, an average 13.59% shorter time to return. Blackjack (p = 0.222), probability (p = 0.324), and video poker (p = 0.012) sessions on average did not differ significantly from slot machine sessions.

Discussion

The DSM-5 operationalizes chasing as often returning to the casino another day (American Psychiatric Association et al., 2013), termed between-session chasing. As a diagnostic criterion, it is arguably the only behaviourally-observable item used in the identification of Gambling Disorder, and it is considered a defining hallmark of problematic gambling (Gainsbury et al., 2014). By analyzing timestamped online gambling data, our analyses refine the understanding of between-session chasing by measuring the time taken for gamblers to return to the platform after winning or losing sessions, across a number of different gambling products. Overall, the fixed factors alone, which included outcome and game type, as well as the time-related nuisance variables, only explained a small portion (5%) of the variance in the time to return. In combination with the level 2 random factors that allowed the degree of win and loss chasing to differ randomly across subjects, 39% of the variance in the time to return was explained. This moderate effect highlights the high degree of individual variation in chasing behaviours. On an aggregate level, we saw that gamblers on most game types returned more slowly after losing sessions, and returned more quickly after winning sessions. These findings are consistent with previous research that measured the interval between sessions for land-based casinos, betting shops, and online horse-racing games (Forrest & McHale, 2016; Kainulainen, 2020; Narayanan & Manchanda, 2012). In our data, roulette was a notable exception: this game had the longest intervals between sessions overall, but was the only game to show loss chasing as a function of greater amounts lost, as well as significantly steeper slopes (in comparison to slot machines) for both loss and win chasing.

In sharp contrast to loss chasing, win chasing – defined as a faster time to return as a function of the previous amount won – was a consistent pattern here across all game types. In a previous field study from Swiss land-based casinos, windfall wins carried over to increase the amount bet on the subsequent casino visit (Rüdisser, Flepp, & Franck, 2017). One possible explanation for these win chasing patterns is the so-called house money effect (Thaler & Johnson, 1990), which describes increased risk-taking because windfalls are not yet internalized as the gamblers' own funds; any loss of such windfalls would not hurt as much as the loss of one's own funds (Peng, Miao, & Xiao, 2013). As a cognitive explanation, one might expect such effects to be short-lived, whereas we observe between-session chasing of wins over the order of days, by which time the win should have been internalized as one's own. In our view, this may point to other explanations, such as a wealth effect, by which an increase in personal wealth enables larger spending in the future (Mehra, 2001). In the context of gambling sessions, prior wins increase financial resources to allow a faster time to return.

In contrast, we did not observe faster return times after losing sessions for most game types. With a greater loss, gamblers took longer to return to the website, indicating an absence of loss chasing in the aggregate data. In the break-even effect, individuals typically increase risk-taking when losing in an effort to recover their losses, but this should only occur when the losses are recoverable (Thaler & Johnson, 1990). In our data, the time intervals to return did not get faster with increasing amounts lost (except for roulette, discussed below), and this might be because further risk-taking did not offer the potential to break-even. We note that this explanation requires significant cognitive processing, to calculate the current difference from one's reference point (e.g., their starting account balance (Imas, 2016)), and the possible impact of a large win to offset that difference. Alternatively, longer return times after losing could be driven by financial constraints: significant losses may simply deplete financial resources to continue gambling (i.e. the converse of the ‘wealth effect’ described above for win chasing). Times to return may also be exogenously influenced by financial factors such as the next payday (Dahan, 2019). In future research, these explanations could be disambiguated by merging gambling data with banking information (Muggleton et al., 2021): for example, if the longer return times are due to insufficient funds, gamblers may be more likely deposit funds into their gambling account following pay days, which would be visible in the banking data.

Slot machines are considered to have among the highest risk potential for any form of gambling (Meyer, Fiebig, Häfeli, & Mörsen, 2011). Accordingly, we expected slot machine sessions to be associated with the shortest times to return. Overall, we found little support for this hypothesis: times to return did not differ significantly between blackjack, probability, video poker, and slot machines. Notably, roulette displayed shorter times to return than slot machines, as a function of both losses and wins, and roulette was the only game type where time to return decreases as a function of the prior amount lost. This apparent uniqueness of roulette should be considered in the context of roulette having the longest overall times to return of any product type, as well as a smaller net loss than other games (Table 1). These differences may render roulette gamblers more sensitive to the moderation by prior outcome, such that each unit of loss would result in larger change in time to return. In terms of structural characteristics, we note that these effects pertain to online roulette, and this is a faster game than land-based (i.e. casino) roulette, and is also accompanied by more intense audiovisual feedback. There is limited research considering the specific product risk associated with online roulette, or indeed for any individual types of eCasino products. It is possible that online gamblers who prefer roulette may display distinct psychological characteristics; for example, Bonnaire, Bungener, and Varescon (2009) reported that gamblers who preferred table games, including roulette, showed lower levels of depression and alexithymia compared to gamblers who preferred slot machines and racetrack gambling. Our findings for roulette arose from exploratory analyses, and as our predictions were of intensified chasing on slot machines, these findings for online roulette clearly warrant replication. Nevertheless, they do provide evidence for an overarching hypothesis that chasing tendencies differ by product category, which is relevant to developing algorithms for detecting high-risk gambling from behavioural data (Edson et al., 2023; Ghaharian, Abarbanel, Kraus, Singh, & Bernhard, 2023). We also recognize that gamblers at risk for gambling problems typically engage with a range of product types (LaPlante, Nelson, & Gray, 2014) such that future work in this area should give attention to both the expressions of chasing on specific gambling products, but also the characteristics of gamblers who chase.

In this study, we quantified the DSM-5 criterion for chasing – the tendency to return another day (“to get even”) – as the time interval between successive online gambling sessions. Previous research (Auer & Griffiths, 2022; Challet-Bouju et al., 2020; Perrot et al., 2018) has focused on increased spending or deposits over time as expressions of chasing. Our study operationalizes the diagnostic item for gambling disorder to derive a concrete behavioural indicator for high-risk gambling. This approach further allows for the characterization of chasing after wins and losses, showing how between-session chasing varies by session outcome.

A number of limitations should be noted. Our data back to 2014–2015 and the online gambling landscape has continued to evolve since that time. We lack demographic descriptives for this de-identified dataset. As with most analyses of behavioural tracking data, it is possible that customers within our dataset held accounts on other gambling platforms (The Behavioural Insights Team, 2021), and visits to other websites would not be represented in our time to return variable. Mitigating this limitation, PlayNow is the only licensed online gambling website in BC, compared to other jurisdictions with many licensed operators. Second, sessions were defined based on a cut-off of 30 min of inactivity on the website, which is somewhat arbitrary. For example, a gambler may sporadically play over the course of a day, with breaks of an hour or longer; should this be classified as one session or several? In principle, the same issue can arise in land-based casinos, e.g., when visiting the bar or ATM. Another limitation is that our analyses focus on time to return as a specific expression of between-session chasing, which may alternatively be measured in the bet volume over successive sessions (Auer & Griffiths, 2022; Flepp et al., 2021). Further behavioural markers exist to characterize within-session chasing tendencies (Chen, Doekemeijer, Noël, & Verbruggen, 2022; O’Connor & Dickerson, 2003). We are exploring these alternative expressions in ongoing research, which also incorporates self-exclusion data (Deng, Lesch, & Clark, 2021; Finkenwirth et al., 2021) as a marker of disordered gambling. Lastly, as noted by a reviewer, mixed sessions represent an interesting instance of a multiple (or chained) reinforcement schedule (Saini, Miller, & Fisher, 2016) controlled by the participant. Even single-category sessions could still involve the gambler switching between specific games, such as different slot machines. The product categories, and within-category products, will vary in structural characteristics, some of which are highly relevant to chasing; for example higher volatility games are associated with longer losing streaks, which may prompt switching products (Delfabbro, King, & Parke, 2023). Future research may drill down further on these session characteristics, viewed from the perspective of operant behaviour.

Funding sources

The Centre for Gambling Research at UBC is supported by the Province of British Columbia government and the British Columbia Lottery Corporation (BCLC; a Canadian Crown Corporation). LC holds a Discovery Award from the Natural Sciences and Engineering Research Council (Canada). KZ held the Graduate Fellowship in Gambling Research, a fellowship supported by the BCLC and adjudicated by the UBC Faculty of Arts.

Author's contributions

KZ conceptualized ideas, analysed data, and drafted the manuscript. JR guided data analysis and manuscript review & editing. XD contributed to data management and server maintenance. TL contributed to conceptualization, data analysis, and manuscript review. LC supervised the project and contributed to manuscript review & editing. All authors except for JR did not have access to data. This study used a secondary dataset provided by BCLC to the Centre for Gambling Research, and JR's guidance on the analysis was based on a simulated dataset. All authors have approved the final manuscript.

Conflict of interests

KZ held the Graduate Fellowship in Gambling Research (2021–2022), a fellowship supported by the BCLC and adjudicated by the UBC Faculty of Arts.

LC is the Director of the Centre for Gambling Research at UBC, which is supported by funding from the Province of British Columbia and the BCLC. The Province of BC government and the BCLC had no role in the preparation of this manuscript, and imposed no constraints on publishing. LC has received a speaker/travel honorarium from the International Center for Responsible Gaming (US) and Scientific Affairs (Germany), and has received fees for academic services from the International Center for Responsible Gaming (US), GambleAware (UK), Gambling Research Australia, and Gambling Research Exchange Ontario (Canada). He has been remunerated for legal consultancy by the BCLC. LC receives an honorarium for his role as Co-Editor-in-Chief for International Gambling Studies from Taylor & Francis, and he has received royalties from Cambridge Cognition Ltd. relating to neurocognitive testing. KZ and LC have not received any further direct or indirect payments from the gambling industry or groups substantially funded by gambling. The remaining authors declare no conflict of interest.

Acknowledgements

The dataset for this study was provided to the researchers by the BCLC, under a Non-Disclosure Agreement that prohibits further data sharing. We would like to thank the Social Responsibility and Data Analytics teams at BCLC for their willingness to share the data, and technical support to de-identify and transfer the data.

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1

(e^{(outcome+outcome*Roulette)}1)*100.

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    • Search Google Scholar
    • Export Citation
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The author instructions are available in PDF.
Please, download the file from HERE

 

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

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  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
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  • PubMed Central
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  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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